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1 ISSN Vol.02,Issue.17, November-2013, Pages: A Novel Multimodal Biometric Approach of Face and Ear Recognition using DWT & FFT Algorithms K. L. N. SANTOSH 1, CH. RAMBABU 2 1 Research Scholar, Dept of ECE, Gudlavalleru Engineering College, Gudlavalleru, Krishna(Dt), AP, INDIA, santoshsany.k@gmail.com. 2 Asst Prof, Dept of ECE, Gudlavalleru Engineering College, Gudlavalleru, Krishna(Dt), A.P-India, rambabu_ec@yahoo.co.in. Abstract: Biometrics recognition tool has great impact in both research and practical implementation of biological security or authentication systems. But as technology is growing, parallel to that the efficiency of these biometric recognition tools also have to be on high standards. Hence the multi modal analysis of biological recognition systems came in to consideration. Automatic face and ear recognition systems have currently reached high hit rates. In this paper, performance evolution of face and ear recognition is obtained by considering various facial and ear images. The objective is to have a similar procedure fo r both face and ear extraction. Here the RGB image is converted to gray scale image initially and then by considering discrete wavelet transformation & Fast Fourier transformation we obtain feature extraction. Finally there will be an acknowledgement with a match or mismatch of test image. The FRR and FAR are considered for performance evolution. Keywords: Multimodal analysis, Face Recognition, Ear Recognition, DWT& FFT. I. INTRODUCTION Person recognition systems are currently most required biometric aspects. Person identification can be done mainly by using physiological or behavioral biometric system. Face, iris, ear are some of physiological traits. Signature, voice recognition are some examples of behavioral person recognition systems. Face Recognition by a robot or machine is one of the challenging research topics in the recent years. It has become an active research area which crosscuts several disciplines such as image processing, pattern recognition, computer vision, neural networks and robotics. For many applications, the performances of face recognition systems in controlled environments have achieved a satisfactory level. However, there are still some challenging issues to address in face recognition under uncontrolled conditions. The variation in illumination is one of the main challenging problems that a practical face recognition system needs to deal with. It has been proven that in face recognition, differences caused by illumination variations are more significant than differences between individuals. RAMESHA K, K B RAJA [1] provided the performance evolution of face recognition concept using wavelet transformation technique & using Euclidean matching algorithm in Other advanced implementation of the face recognition concept is made by Mr. Dinesh Chandra Jain and Dr. V.P.Pawar [2] using Neural networks methods. Where i) the distance between eyes ii) width of nose etc are considered for face recognition process. This paper mainly focuses on the issue of recognition of a person's identity. It is important mainly for security reason, but it could also be used to obtain quick access to medical, criminal, or any type of records. Solving this problem is important because it could allow personnel to take preventive action, provide better service in the case of a doctor s appointment, or allow a person access to a secure area. II. PROPOS ED MODEL Recently, technology became available to allow verification of "true" individual identity. This technology is based on a field called "biometrics". Biometric access control are automated methods of verifying or recognizing the identity of a living person on the basis of some physiological characteristics, such as fingerprints or facial features, or some aspects of the person's behavior, like his/her handwriting style or keystroke patterns. Since biometric systems identify a person by biological characteristics, they are difficult to forge. What exactly this face detection is that it is least intrusive and fastest biometric technology. The idea for this paper came up while studying the concept of multimodal approach of biological recognition systems. As the technical aspect is on high standards there is an absolute requirement of high efficient and quick recognition systems. (i)now in the process of face detection initially some images are 2013 SEMAR GROUPS TECHNICAL SOCIETY. All rights reserved.
2 K. L. N. SANTOSH, CH. RAMBABU considered and stored in the database. (ii)preprocessing: The images of different sizes are computed to a uniform scale i.e image is represented in 2^n-1 notation.(iii) Feature extraction: It is done by using Discrete wavelet transformation and Fast Fourier transformation by considering the approximation band. Discrete wavelet transformation converts the image in to four sub bands. The example of extracted features of the face image is shown in Figure 1.The DWT provides four sub bands of the actual image. FRR= No. of correct persons rejected Total no. of persons (2) The values of False acceptance rate and False rejection rate will differ depending upon the recognition process. So, based on the implemented algorithm the FAR, FRR values varies. Both these values are to be less in order to have an effective recognition system. Figure 1: Example of extracted features of face image The required information of the person is present in the lower frequency bands. So here the LOW-LOW band is considered for the further recognition process. Discrete wavelet transformation is used to eliminate the illumination effect which can have an active impact in the recognition process. The region of interest has to be considered by neglecting the background in the image. This is achieved by considering the orientation of the image. And the low frequency bands compose of the actual information. Fast Fourier transformation is used for obtaining the features of image. This process will repeat for the test based image that is subject which is under recognition process. (iv) Matching process: Then by using Euclidean/Canberra matching algorithm the obtained features will be matched. Finally there will be an authentication with match or mis match. As this paper deals with concept of multimodal analysis the initial selection of image can be either face or ear. The recognition process holds same for both the face and ear. Figure 2 represents the work flow of the face or ear recognition process. The performance of biometric recognition system can be mainly obtained by considering FRR and FAR. False acceptance rate (FAR) is the rate which provides the information regarding number of wrong persons accepted. Figure 2: Workflow of recognition process III. ALGORITHM Problem definition: A novel multimodal biometric recognition of face and ear using discrete wavelet transformation and fast Fourier transformation. Objective: To obtain high efficiency recognition system by using two physiological biometric subjects. TABLE 1: PROPOSED ALGORITHM FAR= No. of wrong persons accepted Total no. of persons (1) False rejection rate (FRR) is the rate which provides the information regarding positive acceptance of wrong persons.
3 A Novel Multimodal Biometric Approach of Face and Ear Recognition using DWT & FFT Algorithms A. Advantages Of Ear Biometrics In this paper ear is considered as another biometric recognition tool because of the following advantages as mentioned below, 1. Unlike the fingerprint and iris, it can be easily captured from a distance without a fully cooperative subject. 2. Unlike the face, the ear is a relatively stable structure that does not change much with the age and facial expressions. The shape does not change due to emotion as the face does, and the ear is relatively constant over most of a person s life. 3. The ear s smaller size and more uniform color are desirable traits for pattern recognition. Hence, based on these facts it is considered that ear recognition will be more significant comparing to that of other tools. IV.S IMULATION RES ULTS 1. In the process of simulation initially after obtaining image of face and ear Figure 3 represents the acknowledgement as match if both selected images are similar. Here i represent the predefined image and ii represents the test image. While both image features will be extracted by using discrete wavelet transformation and then after obtaining the image pattern based on matching process we will have the acknowledgement. Figure 4: Acknowledgement for different images 3. The next consideration is if the person is same but with different facial expressions then the acknowledgement can be mis match. This is the misclassification problem. Fig 5. Represents the possibility of acknowledgement for same person with different expressions. If the selected image features are nearly similar to that of the predefined image i.e the best possibility match will be obtained, then we will have an acknowledgement with a match. Figure 3: Acknowledgement for similar images 2. Now as the concept of misclassification is considered if both the images are different then we will have an acknowledgement as represented in Figure 4. Figure 5: Acknowledgement for misclassification
4 K. L. N. SANTOSH, CH. RAMBABU 4. For misclassification problem the ear recognition tool is included in the recognition process. Figure 6. Represents the match for similar ear images. of persons accepted falsely. Based on these two parameters general biometric recognition performance is calculated. The value of false rejection rate should be low, as the required functionality in any biometric recognition system is reduced false rejection rate. Similarly false acceptance rate also plays a vital role. Hence for any biometric recognition systems the value of false rejection rate and false acceptance rate are more considered. Table 2 provides the various values of false acceptance rate and false rejection rate for varying threshold values. Now the graphical representation of false rejection rate and fa lse acceptance rate are shown in Figure 7.The obtained graph depicts the performance of recognition system of Face & ear. TABLE 2: REPRESENTS THE VALUE OF FRR&FAR Figure 6: Represents image matching As the images selected are similar then we will have authentication as a match. The other options in menu are delete database; this will permanently remove the available stuff which is stored. The final option is EXIT which will terminate the simulation process. Now by considering false acceptance rate and false rejection rate the performance of recognition process can be observed. IV. RES ULT ANALYS IS False rejection rate & false acceptance rate values are shown in the table below. Table 2 provides the values of false rejection rate and false acceptance rate. Values of FRR & FAR are obtained by considering the number of images of persons rejected wrongly and number of images Figure7: Represents the FRR & FAR V.CONCLUS ION Face & Ear recognitions are challenging issues in the field of image analysis and computer vision that has received a great deal of attention over the last few years because of its many applications in various domains. Research has been conducted vigorously in this area for the past four decades or so, and though huge progress has been made, encouraging results have been obtained and current face recognition systems have reached a certain degree of maturity when operating under constrained conditions; however, they are far from achieving the ideal of being able to perform adequately in all the various situations that are commonly encountered by applications utilizing these techniques in practical life. The multimodal analysis developed here can be further preceded by considering the images of ear covering with hair growth. This paper deals with the normal images of face and ear. So, further study and research can be made on above mentioned special considerations.
5 A Novel Multimodal Biometric Approach of Face and Ear Recognition using DWT & FFT Algorithms VI. REFERENCES [1] Ramesha k and K B Raja, Performance evolution of face recognition based on DWT and DT- CWT using matching algorithms International Journal on computer science and Engineering 2011, [2] Mr. Dinesh Chandra jain and Dr. V.P.Pawar, A novel approach of face recognition using neural networks Volume 2 issue [3] Srinivasa murthy H N, Roopa M, Efficient face recognition algorithm using DWT and FFT. Volume 3, issue 6, Page no: [4] Rajesh M Bodade and Sanjay N Talbar, Ear recognition using Dual tree complex wavelet transformation International Journal of Advanced computer science and applications [5] K.H.PUN,Y.S.MOON, Recent advancements in ear biometrics Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition (FGR 04). Author s Profile Mr.K.L.N.Santosh, obtained his B.Tech degree (ECE) in the year 2011 from N.C.E.T. He is pursuing M.Tech (D.E.C.S) in Gudlavalleru College of engineering and technology affiliated to J.N.T.U. KAKINADA. He is interested in the field of image processing and communication systems. Mr.Ch.Rambabu, obtained his B.Tech degree from V.R.Sidhardha engineering college affiliated to Nagarjuna University, and obtained his M.Tech degree from Gudlavalleru Engineering College affiliated to J.N.T.U. Hyderabad. He is having 6 years teaching experience interested in the field of wireless communication and embedded systems. [6] Kyong Chang, Kevin W. Bowyer, and Sudeep Sarkar, Barnabas Victor, Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics. [7] M. Ali, M. Y. Javed, A. Basit, Ear Recognition Using Wavelets, Proceedings of Image and Vision Computing New Zealand 2007, pp
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