Scientific Journal of Impact Factor(SJIF): 3.134 International Journal of Advance Engineering and Research Development Volume 2,Issue 7, July -2015 e-issn(o): 2348-4470 p-issn(p): 2348-6406 Iris Image Categorization for Efficient Large-Scale Iris Classification Anjali Hake 1, Pradeep Patil 2 1 Department of Computer Engg., V.P.C.O.E, Baramati 2 Department of Computer Engg., V.P.C.O.E., Baramati Abstract Iris recognition is an automated method that uses pattern-recognition techniques for biometric identification. The aim of iris image classification is to find common texture primitive in the same category of different subject and classify them to an application specific category. A Hierarchical Visual Codebook (HVC) proposed by Zhenan Sun is used to extract the texture primitives of iris images. Vocabulary Tree (VT) and Locality-constrained Linear Coding (LLC) is used as an integration of Bag-of-words (Bow) models. A K-means clustering is used for hierarchical representation of iris images. The application specific categories such as iris liveness detection, coarse-tofine iris identification and race classification is performed. Keywords-K-means; Visual Codebook; Support Vector Machine; race classification; Vector Quantization. I. INTRODUCTION Biometrics means life measurement. It analyses the characteristics such as Fingerprints, eye retina, iris, facial pattern, DNA etc. It can be an authentication (Verification) system or Identification System. Iris recognition is one of the consistent accurate, fast and secure biometric techniques for human identification. The system captures an image from an individual s eye. The iris in the image is then segmented and normalized for feature extraction process and then matching or classification is performed. Researcher s has taken iris recognition into consideration as one of the common methods of identification like passwords, keys or credit cards. In evolution of the authentication systems, password making them subject to problems such as forgetting the password and passwords being stolen. One way to overcome the harms of authentication is to utilize biometrics traits [1]. Iris has been preferred due to its accuracy, reliability and simplicity as compared to other biometric. The iris is surrounded by the sclera, a white region of connective tissue and blood vessels. The iris and the pupil is covered by a clear covering known as the cornea [2]. It displays rich texture determined by distinctive minutes. Such iris texture is commonly thought to be highly discriminative between eyes and stable over individual's lifetime, which makes iris particularly useful for personal identification. In order to recognize individuals the system uses texture information of the iris. Figure 1: Eye image [2] An approximation of its statistical complexity in a sample of the human population reveals distinction corresponding to some hundred self-governing degrees-of-freedom. The significant application is to match an individual s biometrics beside a database of biometrics or classify them accordingly. Iris recognition is defined as same class such that different subjects with dissimilarity could be identified. But some application want to determine the similarity between iris images to classify them into categories i.e. live or fake, Asian or Non- Asian etc. The classification of iris image helps to speed @IJAERD-2015, All rights Reserved 132
up large scale iris identification [1]. In this paper, two applications are consolidated into a framework for classification of iris images using Hierarchical Visual Codebook method. This method reduces the root level error accumulation. This paper is organized as follows: Section II describes Literature Survey. Section III describes proposed method. Section IV describes datasets available for iris image. Section V shows the result. Section VI concludes the paper. II. LITERATURE SURVEY This section provides a brief survey on each of these areas. An iris recognition system consists of four modules: (i) Image acquisition, (ii) Iris localization, segmentation and image normalization, (iii) Iris feature Extraction and (iv) Iris Classification. The procedure of capturing the iris images is known as enrolment. There are some enrolment devices such as iris sensor and mobile iris camera. A study of the image acquisition wavelength of acknowledges components of the iris, and identified the significant role of iris pigmentation [3]. Iris segmentation refers to the procedure of extracting features that gives information of iris pattern. Segmentation techniques such as Hough transform and integro-differential operator can be used. A texture-based process to predetermine iris is used and Multi scale 2D Gabor Wavelet transform has worn to create a 256-byte iris code. Hamming distance is next used as a measurement to establish the proximity of two iris codes. The integro-differential operator, which acts as a circular edge detector, is employed for determining the upper and lower eyelids as well as the inner and outer boundaries of the iris [4]. The Laplacian of a Gaussian filter has been used to take out features as of the iris image. A Hough transform-based method has used to fragment the iris. Also, the upper and lower of the eyelids are approximated by parabolic curves. Matching has made by means of the normalized correlation among the testing and training images. Hough Transform is used for detecting the iris and pupil boundary [5]. Normalization is performed to acquire all the images in a usual form appropriate for processing. Feature extraction is performed with an application of Laplacian of Gaussian filter at different resolution. Some of the techniques for feature encodings are 1-D Log-Gabor filter, Multiscale 2-D Gabor Wavelet coefficient, LOG filter etc. Iris classification defines the class label of each iris images to find the similarity between different subjects such as Live-Fake iris images, Asian and Non-Asian images. There are two ways to check the liveness of iris images i.e. special feature of iris sensor and texture analysis. Fake iris pattern has four subsets i.e. Plastic, Contact lens, Synth and print. The method FFT (Fast Fourier Transform) is used in order to check the printed iris pattern [6]. An identification of racial attribute of image is need of many applications, such as forensics, intelligent marketing, etc. The multichannel 2-D Gabor filter is used to extract the global texture feature from iris and then to learn a discriminating classifier AdaBoost is used. Iris Texton is used to classify Asian and non-asian subjects [7]. The iris liveness detection based on quality can be assessed by following properties: focus, motion blur, occlusion etc. The best performing features using Sequential Floating Feature Selection (SFFS) algorithm [8]. The iris is unique biometric then it can be used as to identify large population in many commercial and government application. Z. Wei et al. [9] proposed to detect iris images wearing contact lens. III. PROPOSED WORK The proposed system is organized into two phase, preprocessing phase and classification phase respectively. The system architecture is as shown in figure 2. A texture pattern representation method known as HVC is described in this section. The system contains four modules: iris image preprocessing, feature extraction, iris image representation based on HVC method, iris image classification. @IJAERD-2014, All rights Reserved 133
Figure 2: System architecture A. Preprocessing phase: Iris image preprocessing is performed to enhance the image. Input images not only contain useful information but also contain noise. The noise in iris image is may be due to the eyelid, eyelashes, poor illumination etc. Preprocessing must be performed to localize, segment and normalize the iris zone. The phase includes segmentation of the iris region from original iris image and normalization of the iris regions into coordinate system. 1. Segmentation Edge detection is a fundamental tool used in most image processing applications to obtain information from the image as a precursor step to feature extraction. This process detects object outline and boundaries between objects and the background in the image. Some examples of gradient-based edge detectors are Roberts, Prewitt, and Sobel operators. A Canny edge detector could be used for segmentation. It includes four steps, firstly it smoothes the image to eliminate the noise, then finds the image gradient to highlight regions with high spatial derivative, thirdly the algorithm tracks along these regions and suppresses any pixel that is not at the maximum and the gradient array is reduced by hysteresis. We adopt Canny Edge detector for edge Detection. The Hough transform is an algorithm that can be used to decide the parameters of straightforward geometric items, such as lines and circles, there in an image. The Circluar Hough transform is used to detect the pupilian and iris boundaries. 2. Normalization The size of captured iris image is of distinct size. The same person may have the varying size because of variations in illumination, So once the iris region is successfully segmented from an eye image, the next stage is to normalize the iris region in rectangular block so that it has fixed dimensions in order to allow comparisons. The Daugman rubber sheet model is used for normalization which linearly maps the iris texture in the radial direction from pupil border and creates a dimensionless transformation in the radial direction as well [6]. The purpose of normalization is to make iris images of equal size. B. Classification phase: Once the iris region is normalized in rectangular block then iris features are extracted, HVC is used to represent visual feature and for classification of iris images into different categories. 1. Feature extraction To build a statistical representation and to obtain the common components of texture primitive in different iris images feature extraction is performed. Since in iris recognition feature extraction aims to identify local feature unique to each subject. The proposed system used Scale Invariant Feature Transform (SIFT) descriptors since it provides a generic description of local regions and in image analysis it is the most robust descriptor. 2. Hierarchical Visual Codebook (HVC) [1] Once the visual features are extracted then statistical texture representation using BoW model could be obtained. Visual concepts can be generated in different ways, usually through the extraction of discriminate and invariant descriptors (features) around local primitives like interest points, patches, regions, edges, followed by clustering in order @IJAERD-2014, All rights Reserved 134
to identify clusters in feature space of descriptors. The obtained clusters are considered as visual concepts or visual codeword s. A set of such visual codeword s produces a visual codebook. Traditionally, a visual codebook is learned by unsupervised clustering or vector quantization of feature vectors extracted from the local primitives in the image, often with algorithms such as k-means or robust forest. But visual codebook learning and coding are issues in BoW model. Considering these characteristics of iris image the Hierarchical Visual Codebook is used. The method includes codebook learning phase and feature coding phase. In codebook learning phase, vocabulary tree is used to hierarchically represent a huge amount of visual words by calling K-means clustering. The codebook is denoted as H:{H 1, H 2,., H Lmax }. The maximum number of levels in B is L max and K i is the number of clusters partitioned in the i-th level from a parent node. In the coding phase, to decrease the quantization errors of Vector Quantization coding for VT, the LLC coding is performed. It projects each descriptor into its local-coordinate system by utilizing the locality constraints based on the following criteria: (1) Where B is a single level codebook measures the distance between the visual signal and every vocabulary,. is the element wise multiplication, distance between the visual signal, λ is a constant is used to adjust the importance between reconstruction errors and locality constraint and C=[c 1, c 2,, c n ] is the coding coefficients for X. To show the effectiveness of the proposed iris image classification framework the dataset used and classification applications demonstrated i.e. iris liveness detection and race classification is introduced in Section IV. IV. DATASET AND RESULT In order to find out the performance of the developed iris segmentation approach, publicly available databases which comprise of the images are used. Each database is briefly described as follows: A) CASIA-Iris-Fake[10] The database is developed for iris liveness detection. It contains four subsets, namely Print, Contacts, Synth and Plastic. The IG-H100 iris device is used to capture a huge amount of fake iris images. There are 6000 fake iris image in the genuine dataset, 2950 synthesized iris image in the Synth dataset, 400 images in the Plastic dataset and 640 images in the Print dataset. B) Notre Dame Cosmetic Contact Lenses 2013[11] ND Cosmetic Contact Lenses 2013 dataset consist of iris images of subjects with soft contact lenses, with cosmetic contact lenses and without contact lenses, captured using an LG 4000 and an Iris Guard AD100 iris sensor. The dataset consist 4,200 TIFF files captured from the LG4000 sensor, 900 TIFF images captured from the AD100 sensor, and four metadata files describing the images. C) CASIA-Iris-Race[12] The database is developed for race classification i.e. Asian and Non-Asian subjects. Race database was collected with an handheld device OKI irispassh. It contains 1,200 Asian and 1,200 Non-Asian iris images i.e., 20 images/eye. The proposed method can be evaluated in the context of different datasets collected from CASIA-Iris-Fake and CASIA- Iris-Race as described above. The table below shows database containing number of images and respective type. Table 1: Shows different database with types Sr. No. Database Number of Images Types 1 CASIA-Iris-Fake 10,730.bmp 2 Notre Dame Cosmetic Contact Lenses 2013 5,100.tiff 3 CASIA-Iris-Race 2,400.jpg @IJAERD-2014, All rights Reserved 135
Two experiments are carried out to test the performance of iris liveness detection methods under various conditions: Experiment on the CASIA-Iris-Fake Database: To evaluate the overall performance of iris liveness detection methods, combined the four subset of fake iris images dataset made available by CASI-Iris-Fake is used. The result is shown in table 1. Experiment on the single Database: To evaluate the performance of learned Bow model is tested on individual subset of CASIA-Iris-Fake such as Plastic, Print, Contact, and Synth. The result in table 2 shows CCR for contact is 88.07%, Synth 85.79% and Plastic 83.59% and EER for contact is 0.037%, Synth 0.024% and Plastic 0.016%. Table 2: Performance metrics of Iris Liveness Detection methods on the combined dataset Method CCR % EER% Learned Iris Texton [13] Codebook Learning Computational Cost Feature Extraction Classification 98.93 0.96 Fast 0.69s 0.43ms LLC with SPM [14] 99.59 0.50 Fast 0.77s 0.46ms HVC [1] 99.51 0.69 Slow 1.22s 0.50ms HVC using SIFT [Our Implementation] 84.07 0.03 Slow 25s 2.24ms Table 3: Performance metrics of Iris Liveness Detection of our implementation on the single dataset Database HVC Using SIFT [ Our Implementation] CCR% EER% Contact 88.07 0.037 Synth 85.79 0.024 Plastic 83.59 0.016 V. CONCLUSION The Hierarchical Visual Codebook (HVC) method is used for iris classification. Iterative application of K-means is adopted to generate hierarchically classified irises which make a good sense about hierarchical classification. The method integrates the advantage of Vocabulary Tree and Locality-constrained Linear Coding. It avoids accumulation of errors at root level. The experimental results show that the proposed method CCR (Correct classification rate) is 84.07% and EER (Equal Error Rate) is 0.037%. REFERENCES [1] Z. Sun, H. Zhang, T. Tan and J. Wang, Iris Image Classification Based on Hierarchical Visual Codebook, IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, pp. 1120-1133, No. 6, June 2014. [2] S. Shah and A. Ross, Iris Segmentation Using Geodesic Active Contours, IEEE Transaction On Information Forensics And Security, vol. 4, pp. 824-836, No. 4, December 2009. [3] C. Boyce, A. Ross, M. Monaco, L. Hornak, and X. Li, Multispectral Iris Analysis: A Preliminary Study, Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshop Biometrics, pp. 51-59, June 2006. @IJAERD-2014, All rights Reserved 136
[4] J. Daugman, High confidence visual recognition of persons by a test of statistical independence, IEEE Trans. Pattern Anal. Mach. Intell., vol. 15, pp. 1148-1161, no. 11, Nov. 1993. [5] R. Wildes, Iris recognition: An emerging biometric technology, Proc. IEEE, vol. 85, pp. 1348-1363, no. 9, Sep. 1997. [6] J. Daugman, How iris recognition works, IEEE Trans. Circuits Syst. Video Technol., vol. 14, no. 1, pp. 21 30, Jan. 2004. [7] X. Qiu, Z. Sun, and T. Tan, Learning appearance primitives of iris images for ethnic classification, in Proc. ICIP, vol. 2. San Antonio, TX, USA, 2007, pp. 405 408. [8] J. Globally, J. Ortiz-Lopez, J. Fierrez, and J. Ortega-Garcia, Iris liveness detection based on quality related features, in Proc. ICB, New Delhi, India, 2012, pp. 271 276. [9] Z. Wei, X. Qiu, Z. Sun, and T. Tan, Counterfeit iris detection based on texture analysis, in Proc. ICPR, Tampa, FL, USA, 2008, pp. 1 4. [10] Casia-iris-fake, http://www.cripac.ia.ac.in/people/znsun/irisclassification/casia-iris-fake.rar [11] University of Notre Dame Computer Vision Research Lab. ND Iris Image Databases [online]. Available: http://www3.nd.edu/cvrl/cvrl/dtaa_sets.html [12] Casia-iris-race, http://www.cripac.ia.ac.in/people/znsun/irisclassification/casia-iris-race.rar [13] Z. Wei, X. Qiu, Z. Sun, and T. Tan, Counterfeit iris detection based on texture analysis, in Proc. ICPR, Tampa, FL, USA, 2008, pp. 1 4. [14] J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, Locality constrained linear coding for image classification, in Proc. CVPR, San Francisco, CA, USA, 2010, pp. 3360 3367. @IJAERD-2014, All rights Reserved 137