A Feature-level Solution to Off-angle Iris Recognition

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1 A Feature-level Solution to Off-angle Iris Recognition Xingguang Li,2, Libin Wang 2, Zhenan Sun 2 and Tieniu Tan 2.Department of Automation,USTC 2.Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition,CASIA lxguang@mail.ustc.edu.cn,{lbwang,znsun,tnt}@nlpr.ia.ac.cn Abstract For iris recognition, it is inevitable to encounter a large portion of off-angle iris images in less constrained conditions. This paper proposes a feature-level solution to offangle iris recognition which is less dependent on iris image preprocessing. Firstly, we use geometric features of corneal reflections and multiclass SVM to classify iris images into five categories (i.e., frontal, right, left, up and down) according to the off-angle orientation of iris region. And then a feature learning method based on linear programming is used to select the most effective ordinal features of each iris category. Finally, the input off-angle iris image is recognized with the specific ordinal feature template belonging to the corresponding iris category. Experimental results on the Clarkson Angle database demonstrate that our feature-level solution significantly outperforms the mainstream methods based on off-angle iris image preprocessing.. Introduction Iris recognition has become an important personal identification method in our society. Although most state-ofthe-art iris recognition systems need high-level cooperation of users, it is impossible for users to keep staring at the iris cameras during iris recognition. Therefore, it is natural to capture some off-angle iris images when users do not stare at the camera as shown in Fig.(b). If it is easy to capture high quality iris images in the next second, we can discard off-angle iris images in hand. However, it is difficult to capture qualified iris images in some applications such as iris recognition at a distance. So it is better to recognize offangle iris images to improve the success rate of iris recognition even in controlled conditions. A straightforward idea to solve the problem of off-angle iris recognition is to correct the difference between offangle and frontal iris images in the preprocessing stage of iris recognition. All existing off-angle iris image preprocessing methods are based on an important assumption that Figure. The green lines in (a) and (b) show the ellipse fitting results, (c) The hamming distance between two iris images is.4226 using frontal iris image localization and normalization based on circular model, (d) The hamming distance is.478 using offangle iris image preprocessing based on ellipse model, (e) The traditional iris recognition process and (f) Geometric calibration based iris recognition process. the boundary of frontal iris is circular. The task of geometric transformation is to calibrate ellipse boundary of offangle iris region into circular shape. In practice, only the pupil boundary is used in non-circular fitting because the outer iris boundary at sclera is easily affected by specular reflections or other outliers. To the best of our knowledge, almost all existing off-angle iris recognition methods follow such approach. Li [9] proposed a global calibration method to deal with the distortion associated with off-angle iris images. Daugman [4] detected off-angle iris boundary with discrete Fourier series expansions of the contour data. There are also some other methods based on geometric transformation [] []. The preprocessing method of offangle iris images can improve iris recognition accuracy theoretically. However, we find it is difficult to achieve a good performance in practice due to the challenge of non-circular iris localization and normalization. Firstly, the shape of offangle iris boundary is complex and it cannot be well fitted using fixed non-circular model such as ellipse in some cases. Secondly, the noise in iris regions such as specular reflections, eyeglasses, eyelids and eyelashes may affect the accuracy of non-circular iris localization. Thirdly, the geometric transformation from off-angle iris region to frontal iris region is complex because of the 3D structure of eyeball /3/$3. 23 IEEE

2 (a) (b) (c) (d) (e) Figure 3. The corneal reflection positions of different off-angle categories. (a) Frontal, (b) Left, (c) Right, (d) Up and (e) Down. The relative position between the iris boundaries (red line) and corneal reflections (green box) position are diverse for different iris categories. 2. Off-angle iris recognition based on anglespecific feature learning Figure 2. The flowchart of off-angle iris recognition. (a) The basic idea is to match off-angle iris images with the same category of off-angle templates. (b) Classification of off-angle iris images based on geometric features of corneal reflections and multiclass SVM. (c) In training stage, off-angle category-specific ordinal features are selected. In test stage, the iris images are matched with the same category templates using category-specific ordinal features. however some existing normalization methods are based on 2D iris texture assumption. As shown in Fig.(a) (b), the Hamming distance can be reduced from.4226 to.478 by off-angle iris image preprocessing. it is very slight. This example demonstrates that it is difficult to minimize the difference between frontal and off-angle iris images even after specific image preprocessing. Therefore this paper is motivated to provide a novel solution to the problem of off-angle iris recognition in feature-level rather than pre-processinglevel. The basic idea of our approach is to classify off-angle iris images into multiple categories according to the orientation of eye gaze (i.e., frontal, right, left, up and down) and then learn category-specific ordinal features to match distorted iris texture patterns. Here two techniques are proposed to match off-angle iris images in feature-level. Firstly, we think it is better to use off-angle iris images with the same gaze orientation as the gallery templates to match the input off-angle iris image so that a higher matching score can be guaranteed for intra-class comparisons. Secondly, we think the image features of off-angle iris images should be adaptive to the gaze orientation so specific iris features should be learned to match off-angle iris images. In this paper we use geometric features of corneal reflections to classify off-angle iris images into five categories. The remainder of this paper is organized as follows. Section 2 describes the details of the proposed method. Section 3 provides the experiments and analysis. Section 4 summarizes the paper. An important step in traditional off-angle iris recognition is to calibrate the iris images based on geometric transformation in iris image preprocessing stage. The shape of iris boundaries is changed from circle (frontal angle) to ellipse (off-angle). We argue that it is a grand challenge to match preprocessed off-angle iris images using traditional iris recognition method. In this paper, we propose an alternative feature level solution that differs from the traditional one. The flowchart of our method is illustrated in Fig. 2. The basic idea of our method is divide and conquer, i.e. classifying off-angle iris images into multiple categories based on the direction of eye gaze and then matching the input image using specific ordinal features of the same category template. In this paper, we define the off-angle iris images as five categories: frontal, left, right, up and down. The motivation is to classify the iris images into different off-angle categories. Within the same off-angle category, the probe iris image is matched with its gallery one. In recognition stage, we select the representative features for specific off-angle category. In summary, our method mainly has three steps, i.e. geometric feature extraction based on corneal reflections, off-angle iris image classification, and category specific feature selection. 2.. Geometric features of corneal reflections The off-angle iris image is caused by gaze direction away from optical axis of camera. Given 3-D head pose orientation and pupil position, their relationship can be directly used to detect the off-angle categories. However, it is intractable to detect the 3-D head pose orientation in single iris image, because the depth information is unknown. Therefore an alternative solution is essential to detect the off-angle categories. A simple yet effective approach uses the relationship between pupil center and position of corneal reflection to estimate the gaze direction that has been used in remote gaze estimation for many years [6]. Some representative cases of off-angle categories are shown in Fig.3. And the relationship between iris boundaries and corneal reflection is diverse for different off-angle categories. It is easily observed that the position of corneal

3 reflections is usually opposite to the gaze orientation. For instance, corresponding to up iris category, the position of corneal reflections are in the nether region of iris. Because the corneal reflections actually refer to virtual images of light sources, it represents the axis of light source. And the optical axes of between camera and light source are approximately parallel. Given the iris localization results and the corneal reflections, their relative position can be used to describe the off-angle categories. In this section, we extract the geometric features of corneal reflection to describe off-angle categories. Initially, it is necessary to detect the corneal reflections. Inspired by [], we use the scalable kernels to detect the corneal reflections. It mainly includes iris image binarization, s- Figure 5. The geometric features about relative position between corneal reflection and the iris. The green point and the red point represent the corneal reflection and the iris center respectively. Figure 4. The corneal reflections detection includes image binarization, scalable kernel construction. (a) Original iris image and (b) The corneal reflection candidates calable kernel construction and corneal reflections selection (as shown in Fig.4). Here, the shape of corneal reflection is modeled as square. The construction of scalable kernel can remove irregular candidate corneal reflections whose shapes are arbitrary. After corneal reflections detection, we extract the geometric features of corneal reflections (CRGF) to describe the relative position between iris centers and corneal reflections. It consists of three aspects: CRGF CRGF 5 describe the relative location between the corneal reflections and the pupil. It includes geometric center coordinates between corneal reflections, the convolution central response, and the distance between the corneal reflections and pupil center. The above features mainly describe the relationship between the corneal reflection and the pupil center. In [], the distance between them is straightway to identify whether the iris image is off-angle or not. The diagram is shown in the part of the geometric features of corneal reflection of Fig. 2. The red point represents the corneal reflection and the two circles represent inner and outer boundaries of iris respectively. It is simply observed that the distance between corneal reflection and pupil center is not robust to the pupil shrinkage and dilation, which is easily caused by ambient illumination variation. So we need to describe the relative position between the corneal reflections and the iris. As depicted in Fig. 5, we define three distances in the vertical, horizontal and radical directions {HMinor, W Minor, d ls,iris }. The ratios defined in Fig. 5 {HRatio, W Ratio, RRatio} are combined with distances to describe the relative position of corneal reflection corresponding to iris. These features constitute the CRGF 6 CRGF. After iris localization, we obtain the inner and outer iris boundary information including circle centers and their corresponding radii. We also define dilation ratio and eccentricity ratio as follows: DRatio = R pupil R iris () ERatio = (x pupil x iris ) 2 +(y pupil y iris ) 2 R iris (2) These features are denoted as CRGF 2 CRGF 9 to describe the iris position itself. All the above features are combined to describe the relative properties between the corneal reflections and iris boundaries Off-angle category classification After geometric features of corneal reflection extraction, there remains the task of classifying iris images into different off-angle categories. This is a typical multiclass classification problem which is always a hot topic in machine learning. The characteristic about multiclass problems is that the number of classes is greater than two. The simple yet attractive strategy is to transfer multiclass problems into multiple binary problems. After such transformation, there are two cases, one-versus-one and one-versus-the-rest, for training sample selection. Here we choose the one-versusone model (can be found more details in [2]). As mentioned above, there are five labels in off-angle categories classification, so we actually need ten classifiers. We use SVM with linear kernel [3] as the single classifier.

4 Then, the outputs of all the classifiers construct a vector which is regarded as codes for an iris image. After encoding, we obtain the label through voting and the label represents the off-angle category. Of course, it is possible to obtain the same number of votes between different labels which is called as uncertain labels. Here, we discard the samples with uncertain label for simplicity Off-angle specific feature learning Given the off-angle categories of iris images, we can avoid the matching in different off-angle condition as shown in Fig. 2. Intuitively, the texture distribution should be d- ifferent in different off-angle categories. For instance, Fig. 3 (b) shows that the left region can be easily affected by eyelid or eyelash. And the texture in the left region can be compressed severely. So it is not appropriate to adopt the unified feature extraction for different off-angle categories. Feature selection is essential to select discriminative features for each off-angle category,which not only improves the performance but also makes the feature extraction more efficient. Inspired by the idea, we adopt a robust regularized linear programming (RRLP) feature selection method [3] to choose features for different off-angle categories. It is defined as: min w + λ N ξ j j= D w i x + ij α + ξ j, j =...N + i= s.t. D w i x ij β ξ j j =...N i= w i,ξ j i =...D, j =...N (3) where w is the vector of feature weights and x + ij denote the ith feature of jth sample. The non-negative constraint means that the feature terms with negative weight is meaningless, because we want to find discriminative features and all the features should be positive to recognition performance. With the non-negative constraint, the l norm penalty term in object function leads to a sparse solution. And w = w i holds with non-negative constraint which makes this model a standard LP problem. α and β define the margin distance and maximum margin will improve the generalization. The output of weights will be sorted descending and the features corresponding to the non-zero weights are chosen as the selected features. After feature selection, specific ordinal features for iris images are extracted corresponding to their off-angle categories. Then, only the iris images belonging to the same iris category are matched. It will greatly reduce the misclassification caused by the comparisons from different off-angle categories. 3. Experiments and analysis 3.. Dataset Q-FIRE databases [8] are organized by Clarkson university where face and iris videos are obtained by video form at distance of 5 to 25 feet. There are various quality degradations, such as out-of-focus, motion blur and eye angle variations in iris videos. In order to testify our framework, we only use first 9 subjects in the subset of eye angle variation which is collected at 7 feet which is named as Clarkson Angle. Compared with other public databases, there are much more iris images with different off-angle categories captured in practical system. Furthermore, the subjects only turn their eyes while keeping their heads fixed straight ahead. So it is easily to detect the eye automatically. The eye regions at the resolution is cropped from the original face image. After iris regions extracted, there are 24,8 iris images and 8 iris classes in total. The numbers per off-angle category, labeled manually, are listed in Table. We adopt the method in [7] to localize and segment the iris images. Because of the quality variation, some of iris images cannot be segmented, the number of which is listed in the last column in Table. These error segmentation iris images are discarded directly and not used in experiments. Table. The number of images of different off-angle categories in Clarkson Angle Frontal Left Right Up Down Error Seg Image Num. 3,522 4,836 4,948 4,95 3,58 3, Method setting 3.2. Geometric calibration In [, 9, ], the geometric calibration is widely applied to transfer an off-angle iris image into a frontal one. The geometric calibration is implemented as following two steps. Firstly, we detect the edge points of pupil boundary by [7] because pupil boundary is clearer. Then, standard least square based ellipse fitting method [5] is used to determine the ellipse parameters: included angle between the major axis of ellipse and the horizontal axis θ e and ratio between the major axis and the minor axis of ellipse Ratio e. The transformation of geometric calibration is defined as: x y = R( θ e )S(Ratio e )R(θ e ) x y (4) The original image I(x, y) is converted to calibrated image I(x,y ) and the pupil boundary is modified as circle. After calibration, we localize iris outer and inner boundaries with two circles.

5 x 3 The number of features: x 3 The number of features: 9 5 x 3 The number of features: x 3 The number of features: 3.9 x 3 The number of features: (a) (b) (c) (d) Figure 6. The feature selection results. (a) Frontal, (b) Left, (c) Right, (d) Up and (e) Down. The numbers in each figure are the selected number of features finally. (e) Feature description In experiments, we adopt the ordinal features [2] to describe the iris textures. Through changing the lobe number, interlobe distance, interlobe orientation and so on, we can obtain much more ordinal features. With feature selection, different ordinal features construct a feature pool. Through the RRLP feature selection, we obtain the category-specific ordinal features. In the test stage, we only extract such ordinal features. Without feature selection, it is hard to s- elect powerful features manually. Two-lobe ordinal features are extracted in normalized iris image and construct the iriscode Evaluation and analysis 3.3. Accuracy of Off-angle category classification To verify the accuracy of off-angle category determination, we define the accuracy of multiclass classification as follow: N i sgn(l p,i,l o,i ) Acc c = N (5) N i sgn(l p,i, ) { x = y sgn(x, y) = otherwise where l p,i and l o,i denote the predictive label based on off angle categories classification and the original label of the ith iris image and N is the number of the iris image with segment results. The numerator in Equation 5 illustrates the result of classification which is equal to its corresponding label will yield +, otherwise. The numerator in Equation 5 remove the uncertainty labels. Three thousands iris images of each off-angle category are used as the training set. If the rest iris images of each off-angle category are used as the test set, the Acc c is 97.5%. In fact, all the iris images, including training and test set, are used in iris recognition. If all the iris images are used as test set, namely combining training set and test set, the Acc c is 97.5%. The high accuracy of off-angle category classification will be helpful for the following recognition Performance improvement Through changing the parameters and locations, we obtain a feature set with more than ten thousands ordinal features. If all the features are extracted before iris encoding, it will be time consuming. The sparsity of feature selection will make the feature extraction efficient. The feature selection results of each off-angle category are shown in Fig. 6. Corresponding to off-angle categories, the weight distributions are different. It proves our assumption for the feature diversity of different off-angle categories. In addition, the selected features are actually sparse corresponding to original feature set. It will increase the speed of feature extraction. Moreover, it ensures the significance of each feature because the features with small weights are directly discarded. For comparison with the traditional methods, we also make the experiments involving different combinations of preprocessing and feature extraction methods. The ROC curves of different algorithms are calculated and shown in Fig. 7. The performance measures, equal error rate (EER) and discriminating index (D-Index), are listed in Table 2 where the method ID implies the combination from different methods. Table 2. Comparison of Recognition Performance on Clarkson Angle Database Method ID Geometric calibration Off-angle classification Feature selection EER D-Index Yes No No 23.78% Yes No Yes 2.% No No No 22.85% No Yes No 4.92% No No Yes 6.8%.24 6 No Yes Yes 2.4% 4.47 Firstly, we can see that the performance based on normal preprocessing is similar to the one based on geometric transformation. It means that the method based on geometric calibration never improves the recognition performance practically, especially for iris images with severe off-angle. It is caused by two reasons. The one is circle shape assumption. In Fig., the difference between frontal and off-angle iris images is slight, so the calibrated iris images are not still aligned well. The other one is less robustness of ellipse fitting. In fact, the included angle between major axes and horizontal axes is changing variously for the same off-angle category. After calibration, it probably brings additional deviations. Secondly, as shown in Fig.7 and listed in Table 2, the method based on off-angle classification outperforms the methods without off-angle classification. It demonstrates

6 False Reject Rate Method ID Method ID 2 Method ID 3 Method ID 4 Method ID 5 Method ID 6 EER Curve In future, we will focus on the work about off-angle iris image recognition acquired at different distances and extend this non-ideal iris recognition in other quality distortions. 5. Acknowledgement This work is funded by National Natural Science Foundation of China (Grant No , ) and International S&T Cooperation Program of China (Grant No.2DFB4) False Accept Rate Figure 7. The ROC Curves of recognition on Clarkson Angle Database corresponding to different methods. The methods ID referring to the combination of different methods are listed in Table 2 that the intra comparison variations are mainly caused by the match between different iris categories. If iris image matching is only imposed in the same category, it will greatly improve the recognition performance. Finally, after the feature selection, the recognition performance can be further improved. On the one hand, our solution outperforms all the other methods. On the other hand, we achieve FRR. when FAR is 5. It is more stable than the method involving only off-angle classification. It indicates that feature selection procedure is helpful to choose discriminantive features for iris categories. All the properties of our solution make off-angle iris image recognition improve largely. The generation of off-angle iris recognition system depends on registering different off-angle categories templates. The iris category can be determined before matching. The templates will be updated if there is no corresponding iris template for the subject. 4. Conclusions In this paper, we have introduced a fundamental methodology of divide and conquer for off-angle iris images recognition. It is comprised of two parts: divide and conquer which imply off-angle classification and angle specific feature learning respectively. Firstly, the off-angle iris images are classified into five categories according to the orientation of eye gaze (i.e., frontal, left, right, up and down). Secondly, we select the representative features for specific off-angle category. It greatly reduces the intra-score variation caused by matching between different off-angle categories. Once off-angle category classification is accurate, it will improve recognition performance even using simple ordinal features. In experiments, our method outperforms the method based on geometric calibration. References [] A. Abhyankar and S. Schuckers. A novel biorthogonal wavelet network system for off-angle iris recognition. Pattern Recognition, 43(3):987 7, 2. [2] C. M. Bishop. Pattern Recognition and Machine Learning. Springer edition, 26. [3] C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27: 27:27, 2. [4] J. Daugman. New methods in iris recognition. IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics, 37(5):67 75, Oct. 27. [5] A. Fitzgibbon, M. Pilu, and R. Fisher. Direct least square fitting of ellipses. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2(5):476 48, May 999. [6] E. Guestrin and M. Eizenman. General theory of remote gaze estimation using the pupil center and corneal reflections. IEEE Trans. on Biomedical Engineering, 53(6):24 33, Jun. 26. [7] Z. He, T. Tan, Z. Sun, and X. Qiu. Toward accurate and fast iris segmentation for iris biometrics. IEEE Trans. on Pattern Analysis and Machine Intelligence, 3(9):67 684, Sep. 29. [8] P. Johnson, P. Lopez-Meyer, N. Sazonova, F. Hua, and S. Schuckers. Quality in face and iris research ensemble (qfire). In IEEE Int l Conf. on Biometrics: Theory Applications and Systems (BTAS), pages 6, Sep. 2. [9] X. Li. Modeling intra-class variation for nonideal iris recognition. In Advances in Biometrics, volume 3832 of Lecture Notes in Computer Science, pages , 25. [] X. Li, Z. Sun, and T. Tan. Comprehensive assessment of iris image quality. In Proc. IEEE Int l Conf. on Image Processing (ICIP), pages 37 32, Sep. 2. [] S. Schuckers, N. Schmid, A. Abhyankar, V. Dorairaj, C. Boyce, and L. Hornak. On techniques for angle compensation in nonideal iris recognition. IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics, 37(5):76 9, Oct. 27. [2] Z. Sun and T. Tan. Ordinal measures for iris recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence, 3(2): , Dec. 29. [3] L. Wang, Z. Sun, and T. Tan. Robust regularized feature selection for iris recognition via linear programming. In Proc. Int l Conf. on Pattern Recognition (ICPR)., 22.