An Enhanced Histogram Matching Approach Using the Retinal Filter s Compression Function for Illumination Normalization in Face Recognition
|
|
- Rosemary Hood
- 6 years ago
- Views:
Transcription
1 An Enhanced Histogram Matching Approach Using the Retinal Filter s Compression Function for Illumination Normalization in Face Recognition Ahmed Salah-ELDin, Khaled Nagaty, and Taha ELArif Faculty of Computers and Information Sciences Ain Shams University Cairo, Egypt ahmedss2003@gawab.com, knagaty@asunet.shams.edu.eg, taha_elarif@yahoo.com Abstract. Although many face recognition techniques have been proposed, recent evaluations in FRVT2006 conclude that relaxing the illumination condition has a dramatic effect on their recognition performance. Among many illumination normalization approaches, histogram matching (HM) is considered one of the most common image-processing-based approaches to cope with illumination. This paper introduces a new illumination normalization approach based on enhancing the image resulting from the HM using the gamma correction and the Retinal filter s compression function; we call it GAMMA-HM-COMP approach. Rather than many other approaches, the proposed one proves its flexibility to different face recognition methods and the suitability for real-life systems in which perfect aligning of the face is not a simple task. The efficiency of the proposed approach is empirically demonstrated using both a PCA-based (Eigenface) and a frequency-based (Spectroface) face recognition methods on both aligned and non-aligned versions of Yale B database. It leads to average increasing in recognition rates ranges from 4 ~ 7 % over HM alone. 1 Introduction Face recognition from outdoor imagery remains a research challenge area, [1]. Evaluations of the state-of-the-art techniques and systems in FRVT2006 [2] conclude that relaxing the illumination condition has a dramatic effect on the performance. Moreover, It has been proven both experimentally [3] and theoretically [4] that the variations between the images of the same face due to illumination are almost always larger than image variations due to change in face identity. There has been much work dealing with illumination variations in face recognition. Generally, it can be classified into two categories: model-based and image-processing based approaches. Model-based approaches derive a model of an individual face, which will account for variation in lighting conditions [5,6,7]. Such algorithms require a training set with several lighting conditions for the same subject, which in addition to their highly computational cost make these algorithms unsuitable for realistic applications [8,9]. A. Campilho and M. Kamel (Eds.): ICIAR 2008, LNCS 5112, pp , Springer-Verlag Berlin Heidelberg 2008
2 874 A. Salah-ELDin, K. Nagaty, and T. ELArif Image-processing-based approaches attempt to normalize the variation in appearance due to illumination by applying image transformations. Recognition is then performed using this canonical form [8,9,10,11]. Compared to the model-based approach, preprocessing has two main advantages: it is completely stand-alone and thus can be used with any classifier. Moreover, it transforms images directly without any training images, assumptions or prior knowledge. Therefore, they are more commonly used in practical systems for their simplicity and efficiency. The Image-processing-based approaches are applied either globally on the whole image or locally over blocks or regions. Local approaches have the disadvantage that the output is not necessarily realistic. Moreover, they are highly dependant on the local distribution of pixels within the image, which make them sensitive to any geometrical effects on the images such as translation, rotation and scaling rather than global approaches which are not affected by such geometrical changes. Histogram matching (HM) is considered one of the most common global imageprocessing-based approaches [11,12,13,14]. Some comparative studies in literature show the superiority of HM among other approaches [8,10]. For example, [8] compares five different illumination normalization approaches, namely histogram equalization HE, histogram matching HM, log transformation LOG, gamma intensity correction GIC and self quotient image SQI over three large-scale face databases which are FERET, CAS-PEAL and CMU-PIE. The results show that HM gives the best results among the four other approaches over FERET and CAS-PEAL, while comes after GIC over CMU-PIE. Moreover, HM has two main advantages: first, it can be applied with any face recognition method, second, it is insensitive to geometrical effects on the image and thus no additional alignment steps are required. Although enhancing the image resulting from the HM can lead to increase the recognition rates over using the HM alone, no attempts have been made to combine the HM with other image enhancement methods for illumination normalization. Also, the compression function of the Retinal filter as an image enhancement method has not been used in the literature. So, it s very interesting to combine the HM with other image enhancement methods as illumination normalization for face recognition. In this paper, we introduce a new illumination normalization approach based on enhancing the image resulting from HM. Four different image enhancement methods are used in this study. They are combined in two different approaches: First, after HM; on the resulting image from HM, Second, before HM; on the reference image before matching the input image on it. In addition, for each approach, we try to further enhancing the results by applying one of these four methods again. Finally, the proposed approach is chosen from these combinations based on the increase in recognition rates against using the HM alone regardless of the following conditions: 1. Face recognition method that the normalization approach is applied with it, 2. Face alignment within the image, 3. Number of training images, and the degree of illumination within these images. This ensures both the flexibility of the proposed approach among different face recognition methods and the ability to apply it on real-life systems in which perfect alignment of faces is difficult to achieve. The verifications of these conditions are described in detail later in this paper. All previous combinations are empirically demonstrated and compared over Yale B database [15] using two holistic face recognition methods, namely, standard Eigenface
3 An Enhanced Histogram Matching Approach 875 method [16] and Holistic Fourier Invariant Features (Spectroface) [17]. These two methods are chosen to represent the two widely holistic-based categories, PCA-based and frequency-based respectively [18]. The rest of this paper is organized as follows: section 2 contains the description of the four image enhancement methods. In section 3, the different approaches of applying these four methods to enhance the image resulting from HM are introduced. Section 4 is dedicated to describe the Yale B database and the 25 training cases. Experimental results showing the best combinations of HM with different image enhancement methods are presented in section 5. Finally, conclusions and future works are presented in section 6. 2 Image Enhancement Methods The principal objective of image enhancement is to process the original image to be more suitable for the recognition process. Many image enhancement methods are available in the literature. Usually, a certain number of trial and error experiments are required before a particular image enhancement method is selected [19]. In this study, four image enhancement methods are chosen. Three of them are common in literature, namely histogram equalization, log transformation and gamma correction, while the fourth method which called the compression function of the Retinal filter [20] is newly suggested to be used as an image enhancement method in this study. 2.1 Histogram Equalization (HE) It s one of the most common image enhancement methods [19]. It aims to create an image with uniform distribution over the whole brightness scale by using the cumulative density function of the image as a transfer function. Thus, for an image of size M N with G gray levels and cumulative histogram H(g), the transfer function at certain level T(g) is given as follows: H ( g) ( G 1) T(g) = (1) M N 2.2 Log Transformation (LOG) The general form of the log transformation [19] is: s = c log(1 + r) (2) Where r and s are the old and new intensity value, respectively and c is a gray stretch parameter used to linearly scaling the result to be in the range of [0, 255]. The shape of the log curve in Fig.1 shows that this transformation maps a narrow range of dark input gray-levels (shadows) into a wider range of output gray levels. The opposite is true for the higher values of the input gray-levels. 2.3 Gamma Correction (GAMMA) The general form of the gamma correction [19] is:
4 876 A. Salah-ELDin, K. Nagaty, and T. ELArif s = c r 1/γ (3) Where r and s are the old and new intensity value, respectively, c is a gray stretch parameter used to linearly scaling the result to be in the range of [0, 255] and λ is a positive constant. In our case, λ is chosen to be greater than 1 (empirically, it s chosen to be four) in order to map a narrow range of dark input values (shadows) into a wider range of output values, with the opposite being true for higher input values, see Fig.1. Fig. 1. Transformation functions of LOG and GAMMA (L: number of gray levels) 2.4 Compression Function of the Retinal Filter (COMP) A Retinal filter [21] acts as the human retina by inducing a local smoothing of illumination variations. It has been successfully used as an illumination normalization step in the segmentation of facial features in [20,22]. In this paper, we tried to use it as an illumination normalization step in face recognition. However, our empirical results over both Eigenface and Spectroface methods show that using the Retinal filter as an illumination normalization step leads to decrease the recognition rates of both methods. One possible reason is that the Retinal filter produces a non-realistic image which in turn may not be suitable for holistic-based face recognition methods. Therefore, in this study, we use only the compression function of the Retinal filter as an image enhancement method since it s applied globally and so produces more realistic image (for more details about the Retinal filter, see [21]). Let G be a Gaussian filter of size with standard deviation σ = 2 [20]. Let I in be the input image and let I G be the result of G filtering of I in. Image X 0 is defined by: I X I The definition of the compression function C is based on X 0 : C G 0 = (4) G (255 + X ) I I + X 0 in = (5) in 0 3 The Enhanced HM Approaches A total of 40 different enhancement combinations using the HM [19] combined with different enhancement methods are considered and compared in this study in order to
5 An Enhanced Histogram Matching Approach 877 enhance the results of applying the HM alone. Our reference image used for HM is constructed by calculating the average image of a set of well-lit images one for each subject which gives, by our experiments, better results than using a single well-lit image. Each of the four enhancement methods is applied in three different approaches; 1) After the HM, 2) Before the HM, 3) Further enhancing 1 and Enhancement After HM Each of the image enhancement methods, discussed in section 2, is applied on the result of HM in order to enhance it, as shown in Fig.2. This give us four combinations, noted by HM-HE, HM-LOG, HM-GAMMA, HM-COMP, corresponding to applying HE, LOG, GAMMA and COMP, respectively, on the result of HM. Fig.3 shows the effect of these combinations on an illuminated face. Average well-lit Reference Image Input Image Histogram Matching (HM) Matched Image Image Enhancement Output Image Fig. 2. Block diagram of applying the image enhancement method after the HM Illuminated HM HM-HE HM-LOG HM-GAMMA HM-COMP Fig. 3. Effects of applying the image enhancement methods after applying the HM 3.2 Enhancement Before HM Opposite to the approach in 3.1, each of the image enhancement methods is applied on the reference image before matching the input image on it, see Fig.4. This give us another four combinations, noted by HE-HM, LOG-HM, GAMMA-HM, COMP-HM, corresponding to applying HE, LOG, GAMMA and COMP respectively, on the reference image. Fig.5 shows the effect of these combinations on an illuminated face. Average well-lit Reference Image Image Enhancement Enhanced Reference Image Input Image Histogram Matching (HM) Output Fig. 4. Block diagram of applying the image enhancement method before the HM
6 878 A. Salah-ELDin, K. Nagaty, and T. ELArif Illuminated HM HE-HM LOG-HM GAMMA-HM COMP-HM Fig. 5. Effects of applying the image enhancement methods before applying the HM 3.3 Further Enhancement Here, we further enhancing the result of each combination using each of the four enhancement methods which give us 8 4 = 32 additional combinations. Fig.6 shows block diagrams for such enhancements. The effects of further enhancement on the HM-GAMMA using each of the four enhancement methods are illustrated in Fig.7. Input image Enhancement approach in 3.1/3.2 Enhanced image Image enhancement Output Image Fig. 6. Block diagram showing the further enhancement of combinations in 3.1 and 3.2 Illuminated HM 1st Enhance. Further Enhancement Using HM-GAMMA HE GAMMA LOG COMP Fig. 7. Effects of further enhancement on both HM-GAMMA and GAMMA-HM combinations using each of the four enhancement methods 4 Descriptions of Database and Training Cases We use the Yale B database [15] frontal images only for studying and comparing the 40 enhancement combinations. It consists of 10 subjects each with 65 (64 illuminations + 1 normal) images. Only 46 out of these 65 images are divided into four subsets according to the angle the light source direction makes with the camera s axis (12, 25, 50, and 77 ). We shall use only these four subsets in our experiments. All images are cropped in two different ways to include only the head portion: 1. Automatic cropping using the face detection function in Intel OpenCV library [23] to produce non-aligned version of the database; we call it YALE B-AUTO. 2. Manual cropping using the landmarks coordinates available on the Yale B website [24] to produce an aligned version of it; we call it YALE B-MANU. These two versions, shown in Fig.8, allow us to test the robustness of each enhancement combination against the geometrical changes in faces within the images. The better enhancement combination is the one that always enhances the recognition results either with or without aligning the faces inside images.
7 An Enhanced Histogram Matching Approach 879 YALEB-AUTO YALEB-MANU Fig. 8. Samples from Yale B auto and manually cropped The images of a subject in each subset are divided into training and testing images as follows: subset 1 is divided into 3 training images and 5 testing images; each of subset 2, 3, and 4 is divided into 4 training images and 8, 8 and 10 testing images, respectively. As a result, the training set consists of 15 images 10 subjects while the testing set consists of the remaining 31 images 10 subjects. Fig.9 shows the training images in each subset, randomly selected, and the light angle of each image. There are 25 different training cases used in the testing, as shown in table 1, in which the normal image is common in all cases. These training cases are chosen to cover both the training with each elementary subset namely subset 1, 2, 3, and 4, and the training with the seven combinations of these subsets where subset 1 is essential in all of them as it contains the lowest illumination. Each elementary subset is composed of training by the normal image and either the vertical, horizontal or both lighting. While each combination is composed of training by the normal image and either vertical lighting or vertical and horizontal lighting. These training varieties help us to test the robustness of each enhancement combination against the number of training images and the changes in illumination direction of these images. The better enhancement combination, the one that always increases the recognition rates regardless of the training case. 5 Experimental Results The aim of these experiments is to choose the best enhancement combination from the 40 different combinations described in section 3. Thus, each combination is applied four different times corresponding to the Eigenface and Spectroface methods over YALE B-AUTO and YALE B-MANU versions. In each time, a combination is tested over the 25 training cases and their average recognition rate is calculated and then compared with the one resulting from applying the HM alone. The best enhancement combination, the one that increases the recognition rates resulting from applying the HM alone in all of the following: 1. Both face recognition methods (Eigenface and Spectroface), 2. Over aligned and non-aligned versions (YALE B- MANU and YALE B- AUTO), 3. In all the 25 training cases. The first condition is to ensure the flexibility of the chosen combination among different face recognition methods. While the second ensures its suitability for real-life systems, in which perfect aligning of the faces inside images is not a simple task. Finally, by ensuring the increasing of recognition rates in all the 25 training cases, it proves that the chosen combination is not affected by either the number of training images or the changes in illumination direction of these images.
8 880 A. Salah-ELDin, K. Nagaty, and T. ELArif Subset 1 Subset 2 Subset 3 Subset 4 Normal Vertical Vertical Horizontal Vertical Horizontal Vertical Horiz & Ver (+ 10, - 10 ) (+ 25, - 25 ) (+ 20, - 20 ) (+ 50, - 50 ) (+ 45, - 35 ) (+ 70, - 70 ) (+ 65, ± 35 ) Fig. 9. Training images for one subject in the four subsets with the light angle of each image Table 1. The 25 different training cases used in testing Subsets nor: normal ver: vertical Elementary Subsets Training Case(train. images/subject) nor only nor + 2 ver nor + 2 ver nor + 2 hor nor + 2 ver + 2 hor nor + 2 ver nor + 2 hor nor + 2 ver + 2 hor nor + 2 ver nor + 2 hor nor + 2 ver + 2 hor hor: horizontal Subsets 1, 2 1, 3 1, 4 1, 2, 3 1, 2, 4 1, 3, 4 1, 2, 3, 4 Seven Combinations Training Case(train. images/subject) nor + 4 ver nor + 4 ver + 2 hor nor + 4 ver nor + 4 ver + 2 hor nor + 4 ver nor + 4 ver + 2 hor nor + 6 ver nor + 6 ver + 4 hor nor + 6 ver nor + 6 ver + 4 hor nor + 6 ver nor + 6 ver + 4 hor nor + 8 ver nor + 8 ver + 6 hor As described in section 3, 32 out of the 40 enhancement combinations are for further enhancement. To see if further enhancement the image leads to further increasing the recognition rates or not, table 2 shows the number of further enhancement combinations that lead to average increasing in recognition rates more than those accomplished by the single enhancement combinations. It s clear from table 2 that further enhancement the image using any of the three traditional enhancement methods namely HE, GAMMA and LOG, doesn t lead to further enhancement in recognition rates of both the Eigenface and Spectroface methods especially in the YALE B-MANU version. Only COMP that s lead to further enhancement in recognition rates of both face recognition methods over the two database s versions. For example, in Spectroface method over the YALE B-MANU (last row in table 2), we can see that when applying HE as further enhancement after each of the eight single combinations, none of these combinations get further increasing in its average recognition rate due to further enhancement it with HE. The same thing is happened when applying either GAMMA or LOG as further enhancement. On other hand, when applying COMP as further enhancement, five out of the eight combinations get further increasing in their average recognition rates after applying the COMP over those accomplished before applying it. As a result, only five out of 40 enhancement combinations are satisfying the three previously mentioned conditions, their effect is shown in Fig.10:
9 An Enhanced Histogram Matching Approach 881 Table 2. The number of combinations that lead to increase the recognition rates after using each of the enhancement methods for further enhancement Face Recognition Method Eigenface Spectroface Further Enhancement Using: Database HE GAMMA LOG COMP (8 combinations) (8 combinations) (8 combinations) (8 combinations) YALE B-AUTO YALE B-MANU YALE B-AUTO YALE B-MANU GAMMA-HM, where gamma is applied before HM. 2. GAMMA-HM-COMP, where gamma is applied before HM, then the result is further enhanced by applying the compression function. 3. HE-HM-COMP, where equalization is applied before HM, then the result is further enhanced by applying the compression function. 4. COMP-HM-COMP, where compression function is applied before HM, then the result is further enhanced by applying it again. 5. HM-HE-COMP, where equalization is applied after HM, then the result is further enhanced by applying the compression function. Illuminated GAMMA-HM GAMMA-HM-COMP HE-HM-COMP COMP-HM-COMP HM-HE-COMP Fig. 10. Effects of the five enhancement combinations that satisfy the three conditions Table 3 (a, b) shows the results of using these combinations with the Eigenface and the Spectroface methods, respectively, over both versions of the Yale B database. It shows the average recognition rate of each combination over the 25 training cases and the difference between it and the average recognition rate of applying the HM alone. It appears from table 3 (a) that using the second enhancement combination, namely GAMMA-HM-COMP, with the Eigenface method gives the best difference of averages over the four other combinations in both database s versions. While in the Spectroface method, table 3 (b) shows that there are no significant differences between using any of the five combinations in each of the database s versions. As a result, we can choose the GAMMA-HM-COMP combination as the best enhancement combination over the 40 different combinations according to the criteria stated above. 5.1 Complexity of the Proposed Approach The GAMMA-HM-COMP approach based on applying three consecutive steps, namely GAMMA, HM and compression function of the Retinal filter. For an N N image, both GAMMA and HM take O(N 2 ). Since the compression function is based on Gaussian filtering the image by applying the 1D Gaussian filter twice, it takes order O(N 2 k), where k is the mask size. But since the mask size is fixed and equal to 15 in our case [12], the overall complexity of the GAMMA-HM-COMP approach remains O(N 2 ) which is equal to the complexity of using the HM alone.
10 882 A. Salah-ELDin, K. Nagaty, and T. ELArif Table 3. Results of using the best five combinations with (a) Eigenface and (b) Spectroface methods over the two Yale B versions. Average recognition rate is calculated over the 25 different training cases. (1: GAMMA-HM, 2: GAMMA-HM-COMP, 3: HE-HM-COMP, 4: COMP-HM-COMP, 5: HM-HE-COMP). (a) Eigenface method YALE B-AUTO YALE B-MANU HM HM Average Recognition Rate Difference of Averages (b) Spectroface method YALE B-AUTO YALE B-MANU HM HM Average Recognition Rate Difference of Averages Conclusions and Future Works This paper introduces a new image-processing-based illumination normalization approach based on enhancing the image resulting from histogram matching using the gamma correction and the Retinal filter s compression function, which we called GAMMA-HM-COMP approach. It is based on three consecutive steps: 1) Applying the gamma correction on the reference average well-lit image, 2) Histogram matching the input image to the result from 1, 3) Applying the Retinal filter s compression function to further enhancing the result of 2. Among 40 different enhancement combinations, GAMMA-HM-COMP approach proves its flexibility among different face recognition methods and suitability for practical/real-life systems in which perfect aligning of the face is not a simple task. It leads to average increasing in recognition rates over HM alone ranges from 4~7% in Eigenface and Spectroface methods using aligned and non-aligned versions of the Yale B database. However, in this study, the compression function of the Retinal filter is newly applied as an image enhancement method. It proves its suitability for further enhancement rather than the other three traditional enhancement methods which are the histogram equalization, gamma correction and log transformation. Our future work is to empirically compare the GAMMA-HM-COMP approach with other best-of-literature illumination normalization approaches, taking into consideration both the flexibility among different face recognition methods and the robustness for the face aligning problem. In addition, the influence of the GAMMA- HM-COMP approach on non-illuminated face images needs to be investigated. References [1] Phillips, P.J., Grother, P., Micheals, R.J., Blackburn, D.M., Tabassi, E., Bone, J.M.: FRVT 2002: Evaluation Report (2003), [2] Phillips, P.J., Scruggs, W.T., O Toole, A.J., Flynn, P.J., Bowyer, K.W., Schott, C.L., Sharpe, M.: FRVT 2006 and ICE 2006 Large-Scale Results. National Institute of Standards and Technology, NISTIR 7408 (2007), [3] Adini, Y., Moses, Y., Ullman, S.: Face recognition: The problem of compensating for changes in illumination direction. IEEE Tran. PAMI 19(7), (1997)
11 An Enhanced Histogram Matching Approach 883 [4] Zhao, W., Chellappa, R.: Robust face recognition using symmetric shape-from-shading Technical report, Center for Automation Research, University of Maryland (1999) [5] Basri, R., Jacobs, D.W.: Lambertian reflectance and linear subspaces. IEEE Trans. on Pattern Analysis and Machine Intelligence 25(2), (2003) [6] Gross, R., Matthews, I., Baker, S.: Eigen Light-Fields and Face Recognition Across Pose. In: Proceedings of the Fifth IEEE international Conference on Automatic Face and Gesture Recognition, Washington, USA (2002) [7] Georghiades, A., Belhumeur, P., Kriegman, D.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans. on Pattern Analysis and Machine Intelligence 23(6), (2001) [8] Du, B., Shan, S., Qing, L., Gao, W.: Empirical comparisons of several preprocessing methods for illumination insensitive face recognition. In: Proceedings ICASSP 2005, vol. 2 (2005) [9] Heusch, G., Rodriguez, Y., Marcel, S.: Local Binary Patterns as an Image Preprocessing for Face Authentication. In: Proceedings of the 7th international Conference on Automatic Face and Gesture Recognition (Fgr 2006), Washington (2006) [10] Santamara, M.V., Palacios, R.P.: Comparison of Illumination Normalization Methods for Face Recognition. In: Third COST 275 Workshop Biometrics on the Internet, Univ. of Hertfordshire, UK (2005) [11] Levine, M.D., Gandhi, M.R., Bhattacharyya, J.: Image Normalization for Illumination Compensation in Facial Images. Department of Electrical & Computer Engineering & Center for Intelligent Machines, McGill University, Montreal, Canada (Unpublished Report, 2004) [12] Yang, J., Chen, X., Kunz, W., Kundra, H.: Face as an index: Knowing who is who using a PDA. Inter. Journal of Imaging Systems and Technology 13(1), (2003) [13] Jebara, T.: 3D Pose estimation and normalization for face recognition, Honours Thesis, McGill University, Canada (1996) [14] Dubuisson, S., Davoine, F., Masson, M.: A solution for facial expression representation and recognition. Signal Process. Image Commun. 17(9), (2002) [15] Georghiades, A., Belhumeur, P., Kriegman, D.: From few to many: Generative models for recognition under variable pose and illumination. In: Proc. IEEE FG 2000, pp (2000) [16] Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), (1991) [17] Lai, J., Yuen, P., Feng, G.: Face recognition using holistic Fourier invariant features. Pattern Recognition 34(1), (2001) [18] El-Arief, T.I., Nagaty, K.A., El-Sayed, A.S.: Eigenface vs. Spectroface: A Comparison on the Face Recognition Problems. In: IASTED Signal Processing, Pattern Recognition, and Applications (SPPRA), Austria (2007) [19] Gonzales, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Addison Wesley Publishing Company, Inc., New York (2001) [20] Hammal, Z., Eveno, N., Caplier, A., Coulon, P.: Parametric models for facial features segmentation. Signal Process 86(2), (2006) [21] Beaudot, W.: The neural information processing in the vertebra retina : a melting pot of ideas for artificial vision, Phd thesis, tirf laboratory, Grenoble, France (1994) [22] Hammal, Z., Massot, C., Bedoya, G., Caplier, A.: Eyes Segmentation Applied to Gaze Direction and Vigilance Estimation. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds.) ICAPR LNCS, vol. 3687, pp Springer, Heidelberg (2005) [23] Intel OpenCV Liberary, [24] Yale B face database,
Improving Spectroface using Pre-processing and Voting Ricardo Santos Dept. Informatics, University of Beira Interior, Portugal
Improving Spectroface using Pre-processing and Voting Ricardo Santos Dept. Informatics, University of Beira Interior, Portugal Email: ricardo_psantos@hotmail.com Luís A. Alexandre Dept. Informatics, University
More informationExperimental Analysis of Face Recognition on Still and CCTV images
Experimental Analysis of Face Recognition on Still and CCTV images Shaokang Chen, Erik Berglund, Abbas Bigdeli, Conrad Sanderson, Brian C. Lovell NICTA, PO Box 10161, Brisbane, QLD 4000, Australia ITEE,
More informationMulti-PIE. Robotics Institute, Carnegie Mellon University 2. Department of Psychology, University of Pittsburgh 3
Multi-PIE Ralph Gross1, Iain Matthews1, Jeffrey Cohn2, Takeo Kanade1, Simon Baker3 1 Robotics Institute, Carnegie Mellon University 2 Department of Psychology, University of Pittsburgh 3 Microsoft Research,
More informationAn Un-awarely Collected Real World Face Database: The ISL-Door Face Database
An Un-awarely Collected Real World Face Database: The ISL-Door Face Database Hazım Kemal Ekenel, Rainer Stiefelhagen Interactive Systems Labs (ISL), Universität Karlsruhe (TH), Am Fasanengarten 5, 76131
More informationSpecific Sensors for Face Recognition
Specific Sensors for Face Recognition Walid Hizem, Emine Krichen, Yang Ni, Bernadette Dorizzi, and Sonia Garcia-Salicetti Département Electronique et Physique, Institut National des Télécommunications,
More informationOutdoor Face Recognition Using Enhanced Near Infrared Imaging
Outdoor Face Recognition Using Enhanced Near Infrared Imaging Dong Yi, Rong Liu, RuFeng Chu, Rui Wang, Dong Liu, and Stan Z. Li Center for Biometrics and Security Research & National Laboratory of Pattern
More informationThe Effect of Image Resolution on the Performance of a Face Recognition System
The Effect of Image Resolution on the Performance of a Face Recognition System B.J. Boom, G.M. Beumer, L.J. Spreeuwers, R. N. J. Veldhuis Faculty of Electrical Engineering, Mathematics and Computer Science
More informationAuto-tagging The Facebook
Auto-tagging The Facebook Jonathan Michelson and Jorge Ortiz Stanford University 2006 E-mail: JonMich@Stanford.edu, jorge.ortiz@stanford.com Introduction For those not familiar, The Facebook is an extremely
More informationMulti-PIE. Ralph Gross a, Iain Matthews a, Jeffrey Cohn b, Takeo Kanade a, Simon Baker c
Multi-PIE Ralph Gross a, Iain Matthews a, Jeffrey Cohn b, Takeo Kanade a, Simon Baker c a Robotics Institute, Carnegie Mellon University b Department of Psychology, University of Pittsburgh c Microsoft
More informationIranian Face Database With Age, Pose and Expression
Iranian Face Database With Age, Pose and Expression Azam Bastanfard, Melika Abbasian Nik, Mohammad Mahdi Dehshibi Islamic Azad University, Karaj Branch, Computer Engineering Department, Daneshgah St, Rajaee
More informationEFFICIENT ATTENDANCE MANAGEMENT SYSTEM USING FACE DETECTION AND RECOGNITION
EFFICIENT ATTENDANCE MANAGEMENT SYSTEM USING FACE DETECTION AND RECOGNITION 1 Arun.A.V, 2 Bhatath.S, 3 Chethan.N, 4 Manmohan.C.M, 5 Hamsaveni M 1,2,3,4,5 Department of Computer Science and Engineering,
More informationCombined Approach for Face Detection, Eye Region Detection and Eye State Analysis- Extended Paper
International Journal of Engineering Research and Development e-issn: 2278-067X, p-issn: 2278-800X, www.ijerd.com Volume 10, Issue 9 (September 2014), PP.57-68 Combined Approach for Face Detection, Eye
More informationA Proposal for Security Oversight at Automated Teller Machine System
International Journal of Engineering Research and Development e-issn: 2278-067X, p-issn: 2278-800X, www.ijerd.com Volume 10, Issue 6 (June 2014), PP.18-25 A Proposal for Security Oversight at Automated
More informationTitle Goes Here Algorithms for Biometric Authentication
Title Goes Here Algorithms for Biometric Authentication February 2003 Vijayakumar Bhagavatula 1 Outline Motivation Challenges Technology: Correlation filters Example results Summary 2 Motivation Recognizing
More information3D Face Recognition System in Time Critical Security Applications
Middle-East Journal of Scientific Research 25 (7): 1619-1623, 2017 ISSN 1990-9233 IDOSI Publications, 2017 DOI: 10.5829/idosi.mejsr.2017.1619.1623 3D Face Recognition System in Time Critical Security Applications
More informationShape Representation Robust to the Sketching Order Using Distance Map and Direction Histogram
Shape Representation Robust to the Sketching Order Using Distance Map and Direction Histogram Kiwon Yun, Junyeong Yang, and Hyeran Byun Dept. of Computer Science, Yonsei University, Seoul, Korea, 120-749
More informationFace Recognition System Based on Infrared Image
International Journal of Engineering Inventions e-issn: 2278-7461, p-issn: 2319-6491 Volume 6, Issue 1 [October. 217] PP: 47-56 Face Recognition System Based on Infrared Image Yong Tang School of Electronics
More informationChapter 6 Face Recognition at a Distance: System Issues
Chapter 6 Face Recognition at a Distance: System Issues Meng Ao, Dong Yi, Zhen Lei, and Stan Z. Li Abstract Face recognition at a distance (FRAD) is one of the most challenging forms of face recognition
More informationFACE RECOGNITION USING NEURAL NETWORKS
Int. J. Elec&Electr.Eng&Telecoms. 2014 Vinoda Yaragatti and Bhaskar B, 2014 Research Paper ISSN 2319 2518 www.ijeetc.com Vol. 3, No. 3, July 2014 2014 IJEETC. All Rights Reserved FACE RECOGNITION USING
More informationOn the Existence of Face Quality Measures
On the Existence of Face Quality Measures P. Jonathon Phillips J. Ross Beveridge David Bolme Bruce A. Draper, Geof H. Givens Yui Man Lui Su Cheng Mohammad Nayeem Teli Hao Zhang Abstract We investigate
More informationNear Infrared Face Image Quality Assessment System of Video Sequences
2011 Sixth International Conference on Image and Graphics Near Infrared Face Image Quality Assessment System of Video Sequences Jianfeng Long College of Electrical and Information Engineering Hunan University
More informationTDI2131 Digital Image Processing
TDI2131 Digital Image Processing Image Enhancement in Spatial Domain Lecture 3 John See Faculty of Information Technology Multimedia University Some portions of content adapted from Zhu Liu, AT&T Labs.
More informationInternational Journal of Modern Trends in Engineering and Research e-issn No.: , Date: 2-4 July, 2015
International Journal of Modern Trends in Engineering and Research www.ijmter.com e-issn No.:2349-9745, Date: 2-4 July, 2015 Illumination Invariant Face Recognition Sailee Salkar 1, Kailash Sharma 2, Nikhil
More informationEffects of the Unscented Kalman Filter Process for High Performance Face Detector
Effects of the Unscented Kalman Filter Process for High Performance Face Detector Bikash Lamsal and Naofumi Matsumoto Abstract This paper concerns with a high performance algorithm for human face detection
More informationPose Invariant Face Recognition
Pose Invariant Face Recognition Fu Jie Huang Zhihua Zhou Hong-Jiang Zhang Tsuhan Chen Electrical and Computer Engineering Department Carnegie Mellon University jhuangfu@cmu.edu State Key Lab for Novel
More informationThe CMU Pose, Illumination, and Expression (PIE) Database
Appeared in the 2002 International Conference on Automatic Face and Gesture Recognition The CMU Pose, Illumination, and Expression (PIE) Database Terence Sim, Simon Baker, and Maan Bsat The Robotics Institute,
More informationAN EFFECTIVE COLOR SPACE FOR FACE RECOGNITION. Ze Lu, Xudong Jiang and Alex Kot
AN EFFECTIVE COLOR SPACE FOR FACE RECOGNITION Ze Lu, Xudong Jiang and Alex Kot School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore ABSTRACT The three color
More informationNon-Uniform Motion Blur For Face Recognition
IOSR Journal of Engineering (IOSRJEN) ISSN (e): 2250-3021, ISSN (p): 2278-8719 Vol. 08, Issue 6 (June. 2018), V (IV) PP 46-52 www.iosrjen.org Non-Uniform Motion Blur For Face Recognition Durga Bhavani
More informationIMAGE ENHANCEMENT. Quality portraits for identification documents.
IMAGE ENHANCEMENT Quality portraits for identification documents www.muehlbauer.de 1 MB Image Enhancement Library... 3 2 Solution Features... 4 3 Image Processing... 5 Requirements... 5 Automatic Processing...
More informationIris Recognition using Histogram Analysis
Iris Recognition using Histogram Analysis Robert W. Ives, Anthony J. Guidry and Delores M. Etter Electrical Engineering Department, U.S. Naval Academy Annapolis, MD 21402-5025 Abstract- Iris recognition
More informationMethodology for Evaluating Statistical Equivalence in Face Recognition Using Live Subjects with Dissimilar Skin Tones
Eastern Illinois University From the SelectedWorks of Rigoberto Chinchilla June, 2013 Methodology for Evaluating Statistical Equivalence in Face Recognition Using Live Subjects with Dissimilar Skin Tones
More informationFACE RECOGNITION BY PIXEL INTENSITY
FACE RECOGNITION BY PIXEL INTENSITY Preksha jain & Rishi gupta Computer Science & Engg. Semester-7 th All Saints College Of Technology, Gandhinagar Bhopal. Email Id-Priky0889@yahoo.com Abstract Face Recognition
More informationIntelligent Face Detection And Recognition Mohd Danish 1 Dr Mohd Amjad 2
IJSRD - International Journal for Scientific Research & Development Vol. 2, Issue 03, 2014 ISSN (online): 2321-0613 Intelligent Face Detection And Recognition Mohd Danish 1 Dr Mohd Amjad 2 1 M.Tech. Scholar
More informationStudent Attendance Monitoring System Via Face Detection and Recognition System
IJSTE - International Journal of Science Technology & Engineering Volume 2 Issue 11 May 2016 ISSN (online): 2349-784X Student Attendance Monitoring System Via Face Detection and Recognition System Pinal
More informationA Comparison of Histogram and Template Matching for Face Verification
A Comparison of and Template Matching for Face Verification Chidambaram Chidambaram Universidade do Estado de Santa Catarina chidambaram@udesc.br Marlon Subtil Marçal, Leyza Baldo Dorini, Hugo Vieira Neto
More informationImage Enhancement in Spatial Domain
Image Enhancement in Spatial Domain 2 Image enhancement is a process, rather a preprocessing step, through which an original image is made suitable for a specific application. The application scenarios
More informationINTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY
INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY A PATH FOR HORIZING YOUR INNOVATIVE WORK NC-FACE DATABASE FOR FACE AND FACIAL EXPRESSION RECOGNITION DINESH N. SATANGE Department
More informationImage Averaging for Improved Iris Recognition
Image Averaging for Improved Iris Recognition Karen P. Hollingsworth, Kevin W. Bowyer, and Patrick J. Flynn University of Notre Dame Abstract. We take advantage of the temporal continuity in an iris video
More informationRemoval of Gaussian noise on the image edges using the Prewitt operator and threshold function technical
IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661, p- ISSN: 2278-8727Volume 15, Issue 2 (Nov. - Dec. 2013), PP 81-85 Removal of Gaussian noise on the image edges using the Prewitt operator
More informationContrast adaptive binarization of low quality document images
Contrast adaptive binarization of low quality document images Meng-Ling Feng a) and Yap-Peng Tan b) School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore
More informationIMAGE ENHANCEMENT - POINT PROCESSING
1 IMAGE ENHANCEMENT - POINT PROCESSING KOM3212 Image Processing in Industrial Systems Some of the contents are adopted from R. C. Gonzalez, R. E. Woods, Digital Image Processing, 2nd edition, Prentice
More informationReal-Time Face Detection and Tracking for High Resolution Smart Camera System
Digital Image Computing Techniques and Applications Real-Time Face Detection and Tracking for High Resolution Smart Camera System Y. M. Mustafah a,b, T. Shan a, A. W. Azman a,b, A. Bigdeli a, B. C. Lovell
More informationFace Image Quality Evaluation for ISO/IEC Standards and
Face Image Quality Evaluation for ISO/IEC Standards 19794-5 and 29794-5 Jitao Sang, Zhen Lei, and Stan Z. Li Center for Biometrics and Security Research, Institute of Automation, Chinese Academy of Sciences,
More informationGlobal and Local Quality Measures for NIR Iris Video
Global and Local Quality Measures for NIR Iris Video Jinyu Zuo and Natalia A. Schmid Lane Department of Computer Science and Electrical Engineering West Virginia University, Morgantown, WV 26506 jzuo@mix.wvu.edu
More informationLane Detection in Automotive
Lane Detection in Automotive Contents Introduction... 2 Image Processing... 2 Reading an image... 3 RGB to Gray... 3 Mean and Gaussian filtering... 5 Defining our Region of Interest... 6 BirdsEyeView Transformation...
More informationVisual Search using Principal Component Analysis
Visual Search using Principal Component Analysis Project Report Umesh Rajashekar EE381K - Multidimensional Digital Signal Processing FALL 2000 The University of Texas at Austin Abstract The development
More informationFingerprint Quality Analysis: a PC-aided approach
Fingerprint Quality Analysis: a PC-aided approach 97th International Association for Identification Ed. Conf. Phoenix, 23rd July 2012 A. Mattei, Ph.D, * F. Cervelli, Ph.D,* FZampaMSc F. Zampa, M.Sc, *
More information3D Face Recognition in Biometrics
3D Face Recognition in Biometrics CHAO LI, ARMANDO BARRETO Electrical & Computer Engineering Department Florida International University 10555 West Flagler ST. EAS 3970 33174 USA {cli007, barretoa}@fiu.edu
More informationFace Detection System on Ada boost Algorithm Using Haar Classifiers
Vol.2, Issue.6, Nov-Dec. 2012 pp-3996-4000 ISSN: 2249-6645 Face Detection System on Ada boost Algorithm Using Haar Classifiers M. Gopi Krishna, A. Srinivasulu, Prof (Dr.) T.K.Basak 1, 2 Department of Electronics
More informationImage Processing Lecture 4
Image Enhancement Image enhancement aims to process an image so that the output image is more suitable than the original. It is used to solve some computer imaging problems, or to improve image quality.
More informationFAKE FACE DATABASE AND PRE- PROCESSING
FAKE FACE DATABASE AND PRE- PROCESSING Aruni Singh, Sanjay Kumar Singh and Shrikant Tiwari Department of Computer Engineering IIT(BHU), Varanasi, India arunisingh@rocketmail.com sks.cse@itbhu.ac.in shrikant.rs.cse@itbhu.ac.in
More informationAutomatic Electricity Meter Reading Based on Image Processing
Automatic Electricity Meter Reading Based on Image Processing Lamiaa A. Elrefaei *,+,1, Asrar Bajaber *,2, Sumayyah Natheir *,3, Nada AbuSanab *,4, Marwa Bazi *,5 * Computer Science Department Faculty
More informationCoE4TN4 Image Processing. Chapter 3: Intensity Transformation and Spatial Filtering
CoE4TN4 Image Processing Chapter 3: Intensity Transformation and Spatial Filtering Image Enhancement Enhancement techniques: to process an image so that the result is more suitable than the original image
More informationRecent research results in iris biometrics
Recent research results in iris biometrics Karen Hollingsworth, Sarah Baker, Sarah Ring Kevin W. Bowyer, and Patrick J. Flynn Computer Science and Engineering Department, University of Notre Dame, Notre
More informationA Novel Approach For Recognition Of Human Face Automatically Using Neural Network Method
Volume 2, Issue 1, January 2012 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: A Novel Approach For Recognition
More informationDigital Image Processing. Lecture # 4 Image Enhancement (Histogram)
Digital Image Processing Lecture # 4 Image Enhancement (Histogram) 1 Histogram of a Grayscale Image Let I be a 1-band (grayscale) image. I(r,c) is an 8-bit integer between 0 and 255. Histogram, h I, of
More informationColored Rubber Stamp Removal from Document Images
Colored Rubber Stamp Removal from Document Images Soumyadeep Dey, Jayanta Mukherjee, Shamik Sural, and Partha Bhowmick Indian Institute of Technology, Kharagpur {soumyadeepdey@sit,jay@cse,shamik@sit,pb@cse}.iitkgp.ernet.in
More informationSingle-Image Shape from Defocus
Single-Image Shape from Defocus José R.A. Torreão and João L. Fernandes Instituto de Computação Universidade Federal Fluminense 24210-240 Niterói RJ, BRAZIL Abstract The limited depth of field causes scene
More informationChapter 2 Transformation Invariant Image Recognition Using Multilayer Perceptron 2.1 Introduction
Chapter 2 Transformation Invariant Image Recognition Using Multilayer Perceptron 2.1 Introduction A multilayer perceptron (MLP) [52, 53] comprises an input layer, any number of hidden layers and an output
More informationLicense Plate Localisation based on Morphological Operations
License Plate Localisation based on Morphological Operations Xiaojun Zhai, Faycal Benssali and Soodamani Ramalingam School of Engineering & Technology University of Hertfordshire, UH Hatfield, UK Abstract
More informationECC419 IMAGE PROCESSING
ECC419 IMAGE PROCESSING INTRODUCTION Image Processing Image processing is a subclass of signal processing concerned specifically with pictures. Digital Image Processing, process digital images by means
More informationA SURVEY ON HAND GESTURE RECOGNITION
A SURVEY ON HAND GESTURE RECOGNITION U.K. Jaliya 1, Dr. Darshak Thakore 2, Deepali Kawdiya 3 1 Assistant Professor, Department of Computer Engineering, B.V.M, Gujarat, India 2 Assistant Professor, Department
More informationSECTION I - CHAPTER 2 DIGITAL IMAGING PROCESSING CONCEPTS
RADT 3463 - COMPUTERIZED IMAGING Section I: Chapter 2 RADT 3463 Computerized Imaging 1 SECTION I - CHAPTER 2 DIGITAL IMAGING PROCESSING CONCEPTS RADT 3463 COMPUTERIZED IMAGING Section I: Chapter 2 RADT
More informationAn Improved Bernsen Algorithm Approaches For License Plate Recognition
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) ISSN: 78-834, ISBN: 78-8735. Volume 3, Issue 4 (Sep-Oct. 01), PP 01-05 An Improved Bernsen Algorithm Approaches For License Plate Recognition
More informationBiometric Recognition: How Do I Know Who You Are?
Biometric Recognition: How Do I Know Who You Are? Anil K. Jain Department of Computer Science and Engineering, 3115 Engineering Building, Michigan State University, East Lansing, MI 48824, USA jain@cse.msu.edu
More informationPreprocessing of Digitalized Engineering Drawings
Modern Applied Science; Vol. 9, No. 13; 2015 ISSN 1913-1844 E-ISSN 1913-1852 Published by Canadian Center of Science and Education Preprocessing of Digitalized Engineering Drawings Matúš Gramblička 1 &
More informationContent Based Image Retrieval Using Color Histogram
Content Based Image Retrieval Using Color Histogram Nitin Jain Assistant Professor, Lokmanya Tilak College of Engineering, Navi Mumbai, India. Dr. S. S. Salankar Professor, G.H. Raisoni College of Engineering,
More informationChapter 17. Shape-Based Operations
Chapter 17 Shape-Based Operations An shape-based operation identifies or acts on groups of pixels that belong to the same object or image component. We have already seen how components may be identified
More informationInternational Journal of Engineering and Emerging Technology, Vol. 2, No. 1, January June 2017
Measurement of Face Detection Accuracy Using Intensity Normalization Method and Homomorphic Filtering I Nyoman Gede Arya Astawa [1]*, I Ketut Gede Darma Putra [2], I Made Sudarma [3], and Rukmi Sari Hartati
More informationGE 113 REMOTE SENSING. Topic 7. Image Enhancement
GE 113 REMOTE SENSING Topic 7. Image Enhancement Lecturer: Engr. Jojene R. Santillan jrsantillan@carsu.edu.ph Division of Geodetic Engineering College of Engineering and Information Technology Caraga State
More informationFeature Extraction Techniques for Dorsal Hand Vein Pattern
Feature Extraction Techniques for Dorsal Hand Vein Pattern Pooja Ramsoful, Maleika Heenaye-Mamode Khan Department of Computer Science and Engineering University of Mauritius Mauritius pooja.ramsoful@umail.uom.ac.mu,
More informationIris Recognition using Hamming Distance and Fragile Bit Distance
IJSRD - International Journal for Scientific Research & Development Vol. 3, Issue 06, 2015 ISSN (online): 2321-0613 Iris Recognition using Hamming Distance and Fragile Bit Distance Mr. Vivek B. Mandlik
More informationImpact of Out-of-focus Blur on Face Recognition Performance Based on Modular Transfer Function
Impact of Out-of-focus Blur on Face Recognition Performance Based on Modular Transfer Function Fang Hua 1, Peter Johnson 1, Nadezhda Sazonova 2, Paulo Lopez-Meyer 2, Stephanie Schuckers 1 1 ECE Department,
More informationFace Detection: A Literature Review
Face Detection: A Literature Review Dr.Vipulsangram.K.Kadam 1, Deepali G. Ganakwar 2 Professor, Department of Electronics Engineering, P.E.S. College of Engineering, Nagsenvana Aurangabad, Maharashtra,
More informationHow Many Pixels Do We Need to See Things?
How Many Pixels Do We Need to See Things? Yang Cai Human-Computer Interaction Institute, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA ycai@cmu.edu
More informationReal Time Word to Picture Translation for Chinese Restaurant Menus
Real Time Word to Picture Translation for Chinese Restaurant Menus Michelle Jin, Ling Xiao Wang, Boyang Zhang Email: mzjin12, lx2wang, boyangz @stanford.edu EE268 Project Report, Spring 2014 Abstract--We
More informationImage Processing for feature extraction
Image Processing for feature extraction 1 Outline Rationale for image pre-processing Gray-scale transformations Geometric transformations Local preprocessing Reading: Sonka et al 5.1, 5.2, 5.3 2 Image
More informationProposed Method for Off-line Signature Recognition and Verification using Neural Network
e-issn: 2349-9745 p-issn: 2393-8161 Scientific Journal Impact Factor (SJIF): 1.711 International Journal of Modern Trends in Engineering and Research www.ijmter.com Proposed Method for Off-line Signature
More informationBlind Blur Estimation Using Low Rank Approximation of Cepstrum
Blind Blur Estimation Using Low Rank Approximation of Cepstrum Adeel A. Bhutta and Hassan Foroosh School of Electrical Engineering and Computer Science, University of Central Florida, 4 Central Florida
More informationVision-based User-interfaces for Pervasive Computing. CHI 2003 Tutorial Notes. Trevor Darrell Vision Interface Group MIT AI Lab
Vision-based User-interfaces for Pervasive Computing Tutorial Notes Vision Interface Group MIT AI Lab Table of contents Biographical sketch..ii Agenda..iii Objectives.. iv Abstract..v Introduction....1
More informationEnhancement of Face Recognition Rate by Data Base Pre-processing
Enhancement of Face Recognition Rate by Data Base Pre-processing Harihara Santosh Dadi #1, P G Krishna Mohan *2 # Department of ECE, JNTU University, Hyderabad, india * Department of ECE, Institute of
More informationA moment-preserving approach for depth from defocus
A moment-preserving approach for depth from defocus D. M. Tsai and C. T. Lin Machine Vision Lab. Department of Industrial Engineering and Management Yuan-Ze University, Chung-Li, Taiwan, R.O.C. E-mail:
More informationMalaysian Car Number Plate Detection System Based on Template Matching and Colour Information
Malaysian Car Number Plate Detection System Based on Template Matching and Colour Information Mohd Firdaus Zakaria, Shahrel A. Suandi Intelligent Biometric Group, School of Electrical and Electronics Engineering,
More informationThe Classification of Gun s Type Using Image Recognition Theory
International Journal of Information and Electronics Engineering, Vol. 4, No. 1, January 214 The Classification of s Type Using Image Recognition Theory M. L. Kulthon Kasemsan Abstract The research aims
More informationAn Efficient Approach to Face Recognition Using a Modified Center-Symmetric Local Binary Pattern (MCS-LBP)
, pp.13-22 http://dx.doi.org/10.14257/ijmue.2015.10.8.02 An Efficient Approach to Face Recognition Using a Modified Center-Symmetric Local Binary Pattern (MCS-LBP) Anusha Alapati 1 and Dae-Seong Kang 1
More informationHuman Identification from Video: A Summary of Multimodal Approaches
June 2010 Human Identification from Video: A Summary of Multimodal Approaches Project Leads Charles Schmitt, PhD, Renaissance Computing Institute Allan Porterfield, PhD, Renaissance Computing Institute
More informationVehicle License Plate Recognition System Using LoG Operator for Edge Detection and Radon Transform for Slant Correction
Vehicle License Plate Recognition System Using LoG Operator for Edge Detection and Radon Transform for Slant Correction Jaya Gupta, Prof. Supriya Agrawal Computer Engineering Department, SVKM s NMIMS University
More informationLibyan Licenses Plate Recognition Using Template Matching Method
Journal of Computer and Communications, 2016, 4, 62-71 Published Online May 2016 in SciRes. http://www.scirp.org/journal/jcc http://dx.doi.org/10.4236/jcc.2016.47009 Libyan Licenses Plate Recognition Using
More informationImage Enhancement using Histogram Equalization and Spatial Filtering
Image Enhancement using Histogram Equalization and Spatial Filtering Fari Muhammad Abubakar 1 1 Department of Electronics Engineering Tianjin University of Technology and Education (TUTE) Tianjin, P.R.
More informationAutomatic Vehicles Detection from High Resolution Satellite Imagery Using Morphological Neural Networks
Automatic Vehicles Detection from High Resolution Satellite Imagery Using Morphological Neural Networks HONG ZHENG Research Center for Intelligent Image Processing and Analysis School of Electronic Information
More informationSCIENCE & TECHNOLOGY
Pertanika J. Sci. & Technol. 25 (S): 163-172 (2017) SCIENCE & TECHNOLOGY Journal homepage: http://www.pertanika.upm.edu.my/ Performance Comparison of Min-Max Normalisation on Frontal Face Detection Using
More informationMAV-ID card processing using camera images
EE 5359 MULTIMEDIA PROCESSING SPRING 2013 PROJECT PROPOSAL MAV-ID card processing using camera images Under guidance of DR K R RAO DEPARTMENT OF ELECTRICAL ENGINEERING UNIVERSITY OF TEXAS AT ARLINGTON
More informationUSE OF IMAGE ENHANCEMENT TECHNIQUES FOR IMPROVING REAL TIME FACE RECOGNITION EFFICIENCY ON WEARABLE GADGETS
Journal of Engineering Science and Technology Vol. 12, No. 1 (2017) 155-167 School of Engineering, Taylor s University USE OF IMAGE ENHANCEMENT TECHNIQUES FOR IMPROVING REAL TIME FACE RECOGNITION EFFICIENCY
More informationA Neural Network Facial Expression Recognition System using Unsupervised Local Processing
A Neural Network Facial Expression Recognition System using Unsupervised Local Processing Leonardo Franco Alessandro Treves Cognitive Neuroscience Sector - SISSA 2-4 Via Beirut, Trieste, 34014 Italy lfranco@sissa.it,
More informationAn Approach for Reconstructed Color Image Segmentation using Edge Detection and Threshold Methods
An Approach for Reconstructed Color Image Segmentation using Edge Detection and Threshold Methods Mohd. Junedul Haque, Sultan H. Aljahdali College of Computers and Information Technology Taif University
More informationEfficient Construction of SIFT Multi-Scale Image Pyramids for Embedded Robot Vision
Efficient Construction of SIFT Multi-Scale Image Pyramids for Embedded Robot Vision Peter Andreas Entschev and Hugo Vieira Neto Graduate School of Electrical Engineering and Applied Computer Science Federal
More informationA Comparison Study of Image Descriptors on Low- Resolution Face Image Verification
A Comparison Study of Image Descriptors on Low- Resolution Face Image Verification Gittipat Jetsiktat, Sasipa Panthuwadeethorn and Suphakant Phimoltares Advanced Virtual and Intelligent Computing (AVIC)
More information1.Discuss the frequency domain techniques of image enhancement in detail.
1.Discuss the frequency domain techniques of image enhancement in detail. Enhancement In Frequency Domain: The frequency domain methods of image enhancement are based on convolution theorem. This is represented
More informationObject Recognition System using Template Matching Based on Signature and Principal Component Analysis
Object Recognition System using Template Matching Based on Signature and Principal Component Analysis Inad A. Aljarrah Jordan University of Science & Technology, Irbid, Jordan inad@just.edu.jo Ahmed S.
More informationSuper resolution with Epitomes
Super resolution with Epitomes Aaron Brown University of Wisconsin Madison, WI Abstract Techniques exist for aligning and stitching photos of a scene and for interpolating image data to generate higher
More information