Software Development Kit to Verify Quality Iris Images
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1 Software Development Kit to Verify Quality Iris Images Isaac Mateos, Gualberto Aguilar, Gina Gallegos Sección de Estudios de Posgrado e Investigación Culhuacan, Instituto Politécnico Nacional, México D.F., México Abstract - This paper proposes an algorithm for the development of an SDK to verify quality on iris images based on ISO/IEC :2005, standard that is being mainly used by several manufacturers of biometric systems based on iris recognition. For the development of this algorithm an assessment is made for each parameter recommended in the standard, with the aim of determine a total quality score of iris images and decide if they have unacceptable, low, medium or high quality, selecting from this way the good ones and thus increase the efficiency of iris recognition systems. The proposed algorithm has been tested with images from a own dataset collection. It is still a need to adopt a method to determine an overall value to the fusion of all the individual feature values. Keywords: Biometrics, iris image quality evaluation. 1 Introduction Currently, iris recognition has become the most reliable biometric system performance in terms of verification and identification of people. Today there are several systems dedicated to iris recognition, however the performance of these systems is affected due to poor quality iris images. The main problems that affect the system are the false accept (FMR) and the false reject (FNMR). If one can detect low-quality biometric samples, the information can be used to initiate the acquisition of new data and improve system performance. For best performance of the development of the SDK, the proposed algorithm refers to the ISO standard, which makes recommendations about the features which must be met by the iris biometric images to determine if you have a suitable quality for specific purposes. The main goal of standardization is to enable harmonized interpretation of quality scores and can differentiate them from the different vendors, algorithms and versions, enabling in this manner a competitive multi-vendor marketplace. As result of the measurement, the same quality measure can be used to selectively improve an operational biometric database by replacing low-quality biometric samples with high-quality samples of the same biometric. 1.1 ISO/IEC :2005 The International Organization for Standardization (ISO) has created the ISO/IEC :2005. [1] in support to the necessity of iris images quality samples, which recommends the assessment of essential characteristics of iris images, giving an overall value between 0 and 100, with 100 being the highest quality and 0 de poorest quality. However, this issue is not specifically defined and is still ongoing research. 1.2 IREX -IQCE The Iris Exchange (IREX) [2] was initiated at National Institute of Standards and Technology (NIST) in support of an expanded marketplace of iris-based applications based on standardized interoperable iris imagery, mainly in support of the ISO/IEC standard. Iris Quality Calibration and Evaluation (IQCE) [3] aims to evaluate the effectiveness of image quality assessment algorithms (IQAAs) that produce a scalar overall image quality in predicting the recognition accuracy of particular comparison algorithms (from the supplier of the IQAA), and of other algorithms. 1.3 Current SDK s Some of the leading vendors in the iris biometric recognition system in the marketplace with their own SDK are LG, NEUROTECHNOLOGY, CROSSMATCH, AWARE, MORPHO, IRITECH, IRISID, KYNEN, L1, and exists different performance between their SDK results, of course they utilize their own algorithms for measurement of the features of the images, and compare some of these SDK with the current development one, compare some of these SDK s may give us information about which one has better performance and create a competitive environment for best results. 2 Proposed algorithm For the development of the SDK, different algorithms must be adopted for measuring the individual characteristics that indicates the standard, and also for the segmentation of the iris in the images. There are several methods to achieve this purpose however is a challenge to select the appropriate one that complies with speed and accuracy for each feature. In this work we started with the identification and location of
2 Figure 1. Proposed Algorithm to the SDK the pupil, because if you find an image that of is not an iris, the SDK automatically rule it out, thus avoiding the image processing passes through the measurement of each feature. The image processing is described in the figure 1. As shown in the algorithm there are many individual characteristics to measure, so it is necessary to give a brief description of the most important ones according to IQCE [2] as following: Usable iris area is defined as the percentage of iris that is not occluded by eyelash, eyelid, specular reflections and ambient specular reflections. Iris pupil contrast is a measure of the image characteristics at the boundary between the iris region and the pupil. Pupil shape is a measure of regularity in pupil-iris boundary. Iris sclera contrast is a measure of the image characteristics at the boundary between the iris region and the sclera. Gaze angle is the deviation of the optical axis of the subject s iris from the optical axis of the camera. Sharpness, defined as the absence of defocus blur, can result from many sources, but in general, defocus occurs when the object is outside the depth of field of the camera. Dilation is defined as the ratio of the pupil radius to iris radius. An image with a high Gray scale spread (good quality) is a properly exposed image, with a wide, well distributed spread of intensity values. Iris shape is defined as the shape of iris-sclera boundary. Iris size is defined as the number of pixels across the iris radius, when the iris boundary is modeled by a circle. Motion blur is defined as the blur cause by motion of the camera or the iris, or both. Once performed the measurement of the characteristics indicated in the algorithm, the system must create a feature vector containing the measurements of each characteristic separately and finally an overall score value should be given according to the ISO/IEC :2005 standard [1] to determine if the image has a poor, low, medium or high quality. 3 Experimental results Tests have been performed with 60 biometric iris images from an own dataset collection, all images were acquired using an LG IRIS ID icam TD100 [11]. The iris images are 480x640 in resolution. 3.1 Pupil Identification and Localization First to locate the iris pupil, is used the method described by Lili Pan [4], using a binarization by selecting an appropriate threshold and finding the center where the true value of pixel intensity is minimal. As shown in Figure 2.
3 Figure 4. Cropped image Figure 2. Binarized image to pupil detection 3.3 Contrast Measurement The next step is to measure the contrast level of pupil and sclera, performing the measurement along the diameter of the iris, obtaining a graph as shown in Figure Gray Scale Spread Measurement Once identified that it is a true image of the iris, the measurement of the first feature 'Gray Scale Spread'. An image with a high GRAY SCALE SPREAD (good quality) is a properly exposed image, with a wide, well distributed spread of intensity values [3]. This is accomplished by performing a histogram as shown in Figure 3. Then the image is cropped for fast image segmentation of the pupil and iris as shown in Figure 4. Figure 5. Contrast in Sclera, Iris and Pupil Figure 3. Histogram wide spread image values 3.4 Pupil and iris Shape Continuing using the algorithm of Lili Pan [4], noting the graph of Figure 5 shows that the intensity values between pupil, iris and sclera vary drastically, based on this fact can be detected edges, result can be seen in Figure 6.
4 Figure 6. Iris and Pupil Segmentation 3.5 Iris Size and Dilation Measurement IRIS SIZE is defined as the number of pixels across the iris radius and DILATION as the ratio of the pupil radius to iris radius [3]. As a result of segmentation by edge detection, we can easily measure the diameter of the pupil and iris, and thus obtain the value of the dilatation. 3.6 Usable Iris Area Measurement USABLE IRIS AREA is defined as the percentage of iris that is not occluded by eyelash, eyelid, specular reflections and ambient specular reflections [3]. Occluded images are another big problem in iris image quality assessment and possibly one of the most difficult features to measure, because it is not possible to adopt a special algorithm. Lili Pan recommends compute the number of pixels in the iris area, then set two gray level thresholds one for detect eyelash and other for detect eyelid [4]. Which throws high error, because in many images the iris has both dark and light parts that may be confused with eyelashes and eyelids. Chunlei Shi recommends selecting the upper rectangle area of the pupil as the ROI, we regard the average gray value of it as the judgment criterion, and then get rid of the occlusion images [6]. By a combination of these methods, we obtain the results observed in Figure 7 (b). 3.7 Sharpness Measurement SHARPNESS, defined as the absence of defocus blur, mainly affects FNMR and FMR, images with low sharpness inflate FMR. [3]. For the measurement of this feature, using the high frequency power of the image to evaluate the degree of focus is a common method in previous research on image focus assessment [ 5,6,7,8 ]. Measuring with a high frequency filter on the ROI (Usable iris Area) as shown in Figure 7(c). (a) Segmented Iris (b) Usable iris Area (c) high frequency filter Figure 7. Measurement of Visible Area and Sharpness 4 Future work It is necessary to adopt algorithms to asses missing features as Motion Blur, Signal to Noise Ratio, Gaze Angle, Interlacing. Papers [ 5,7,10 ] talk about different algorithms for the measurement of these features so they should be tested to check which have the best performance. Once all features have been measured, we obtain a Feature Quality Vector, as show in Figure 1, and the final step is to obtain an Overall Single Score. Not all features have the same weight of importance in the iris recognition system, thus giving an overall score is a problem, a bad quality in a feature with little weight of importance should not greatly affect the overall quality score.the papers [ 7,9,10 ] propose algorithms to obtain a score fusion, also this work intends to use a neuronal network, so continuous research and testing should be done to choose the best algorithm and finally succeed in developing a robust SDK with the best performance. Future tests will be made on dataset collection of standard biometric iris images. 5 Conclusions Iris Usable Area is the feature most important to weight the iris recognition system and is therefore a key factor for the overall score, this because although the image count with good lighting and good sharpness if the iris is obstructed by eyelashes or eyelids, the necessary features for the recognition system never would be obtained for a good performance. so in this way, give priority in importance to all the features measured, where the second most weight of importance is the Contrast, followed by Sharpness, after this the Dilation of the pupil, the Gaze Angle, Interlace, Gray Scale, Spread, Iris Size and finally the, Motion Blur and Signal to Noise Ratio.
5 6 References [1] INCITS/ISO/IEC :2005 Information technology Biometric data interchange formats Part 6: Iris image. [2] National Institute of Standards and Technology NIST., IREX Iris Exchange., [3] Elham Tabassi, Patrick Grother, Wayne Salamon., IREX II IQCE Iris Quality Calibration and Evaluation 2011., Concept, Evaluation Plan and API 6 Version 4.1 Image Group, Information Access Division, Information Technology Laboratory, National Institute of Standards and Technology September 28, [4] Lili Pan, Mei Xie., Research on Iris Image Preprocessing Algorithm., School of Electronic Engineering University of Electronic Science and Technology of China, Chengdu, China, Proceedings ofiscit2005. [5] Z. Wei, T. Tan, Z. Sun and J. Cui., Robust and fast assessment of iris image quality., Lecture Notes in Computer Science, v 3832 LNCS, p , 2006, Advances in Biometrics - International Conference, ICB 2006, Proceedings. [6] C. Shi and L. Jin., A fast and efficient multiple step algorithm of iris image quality assessment., Proceedings of the nd International Conference on Future Computer and Communication, ICFCC 2010, v 2, p V2589-V2593, 2010, Proceedings of the nd International Conference on Future Computer and Communication, ICFCC [7] N. Kalka, J. Zuo, N. Schmid and B. Cukic., Image quality assessment for iris biometric., Proceedings of SPIE - The International Society for Optical Engineering, v 6202, 2006, Biometric Technology for Human Identification III.
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