This paper presents a state-of-the-art iris segmentation framework specifically for non-ideal irises. The framework adopts coarse-to-fine strategy to localize different boundaries. In the approach, pupil is coarsely detected using an iterative search method exploiting dynamic thresholding and multiple local cues. The IntroductionIris is considered one of the most accurate biometrics because of its stability over long time, non-invasiveness, and unique pattern [1]. The accuracy of iris localization, decides the iris texture to be used for feature extraction and thus accuracy of overall recognition system, which makes this stage an important part of iris recognition. Daugman's Integro-Differential Operator (IDO) [1] and Wilde's circular Hough transform based approach [2] are the most significant and well known techniques for iris segmentation. Based on the basic steps in these two methods, many techniques have been proposed with some variations [5,7,9,14]. These classical and other similar approaches achieve excellent accuracy when iris images are of high-quality. However, in real world applications images may contain some types of challenges such as, occlusion, illumination variation, specular reflections, pupil constriction/expansion, image resolution, etc.. The prior art also discloses that almost all of such methods assume that pupillary and limbic boundaries are circular/ elliptical [1,2,14,18]. Recently, researchers discovered that in non-ideal iris images and even in ideal images, some shapes of pupil and iris are not perfect circle or ellipse [11]. Also, sometimes in non-ideal images, gradient on pupillary, iris and eyelid boundaries is not sufficient enough for these models. Thus, performance of such methods degrades significantly in non-ideal images. The other problems are that, these methods are time taking and computationally intensive due to brute-force approach and search of large parameter space. Moreover, parameters which are set to work for one database, may not work for other database. To address these issues, recently, researchers have started focusing towards non-ideal iris segmentation and various methods have been proposed [3][4][5][6][10][11][12][15][16][17] including adaptation of different types of active contours [11][12][13]19]. The active contours require the initial boundary from where it can proceed to localize actual boundary and a large number of iterations is a mandatory requirement, which makes the whole process a bit complex. For a detailed discussion on iris segmentation approaches and current issues, we refer the readers [7].In this paper, we propose an iris segmentation framework 1 which is simple to implement, robust, can work with non-ideal iris images without requirement of database specific parameter tuning. The major contributions include- Highly effective pupil detection module based on a Pupil Candidate Bank (PCB) created using iterative thresholding followed by selection of best candidate by exploiting multiple local features. Coarse approximation of limbi...

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Recently periocular biometrics has drawn lot of attention of researchers and some efforts have been presented in the literature. In this paper, we propose a novel and robust approach for periocular recognition. In the approach face is detected in still face images which is then aligned and normalized. We utilized entire strip containing both the eyes as periocular region. For feature extraction, we computed the magnitude responses of the image filtered with a filter bank of complex Gabor filters. Feature dimensions are reduced by applying Direct Linear Discriminant Analysis (DLDA). The reduced feature vector is classified using Parzen Probabilistic Neural Network (PPNN). The experimental results demonstrate a promising verification and identification accuracy, also the robustness of the proposed approach is ascertained by providing comprehensive comparison with some of the well known state-of-the-art methods using publicly available face databases; MBGC v2.0, GTDB, IITK and PUT.

The security of biometric systems, especially protecting the templates stored in the gallery database, is a primary concern for researchers. This paper presents a novel framework using an ensemble of deep neural networks to protect biometric features stored as a template. The proposed ensemble chooses two state-of-the-art CNN architectures i.e., ResNet and DenseNet as base models for training. While training, the pre-trained weights enable the learning algorithm to converge faster. The weights obtained through the base model is further used to train other compatible models, generating a fine-tuned model. Thus, four fine-tuned models are prepared, and their learning are fused to form an ensemble named as PlexNet. To analyze biometric templates' security, the rigorous learning of ensemble is collected using a smart box i.e., application programming interface (API). The API is robust and correctly identifies the query image without referring to a template database. Thus, the proposed framework excludes the templates from database and performed predictions based on learning that is irrevocable.

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