Conference Paper

A Design of Iris Recognition System at a Distance

Inst. of Autom., Chinese Acad. of Sci., Beijing, China
DOI: 10.1109/CCPR.2009.5344030 Conference: Pattern Recognition, 2009. CCPR 2009. Chinese Conference on
Source: IEEE Xplore


Iris recognition is a powerful biometrics for personal identification, but it is difficult to acquire good-quality iris images in real time. For making iris recognition more convenient to use, we design an iris recognition system at a distance about 3 meters. There are many key issues to design such a system, including iris image acquisition, human-machine-interface and image processing. In this paper, we respectively introduce how we deal with these problems and accomplish the engineering design. Experiments show that our system is convenient to use at the distance of 3 meters and the recognition rate is not worse than the state-of-the-art close-range systems.

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    • "Iris is one of the most investigated and employed biometrics for automatic people recognition, mainly due to the high recognition accuracy it can provide, and to the ease with which it can be acquired [7]. Several iris template protection schemes have been therefore recently proposed [8]. "
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    ABSTRACT: Template protection is an issue of paramount importance in the design of biometric recognition systems. In this paper we present a biometric cryptosystem applied to iris biometrics, where template security is guaranteed by means of a framework inspired by the digital modulation paradigm. Specifically, the properties of modulation constellations and turbo codes with soft-decoding are exploited to design a system with high performance in terms of both verification rates and security, even while dealing with a biometrics characterized by a high intra-class variability such as the iris. The effectiveness of the proposed approach is evaluated by performing tests on the Interval subset of the CASIA-IrisV4 database.
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    • "We use our long-range multi-modal biometric recognition system[5] to obtain high resolution binocular images at a distance under near infrared (NIR) illumination. This imaging system mainly consists of two wide-range web cameras , a narrow-range high resolution NIR camera, a pantilt-zoom (PTZ) unit and a NIR light source. "
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    ABSTRACT: Local descriptor based image representation is widely used in biometrics and has achieved promising results. We usually extract the most distinctive local descriptors for im-age sparse representation due to the large feature space and the redundancy among local descriptors. In this paper, we describe the local descriptor based image representation via a graph model, in which each node is a local descrip-tor (we call it "atom") and the edges denote the relation-ship between atoms. Based on this model, a hierarchical structure is constructed to select the most distinctive local descriptors. Two-layer structure is adopted in our work, including local selection and global selection. In the first layer, L 1 /L q regularized least square regression is adopted to reduce the redundancy of local descriptors in local re-gions. In the second layer, AdaBoost learning is performed for local descriptor selection based on the results of the first layer. We apply this method to long-range personal identi-fication by using binocular regions. Our method can select the distinctive local descriptors and reduce the redundancy among them, and achieve encouraging results on the col-lected binocular database and CASIA-Iris-Distance. Par-ticularly, our method is about 50 times faster than the tradi-tional AdaBoost learning based method in the experiments.
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    • "Therefore face-iris fusion is the most promising strategy in practical applications, which is both user-friendly and accurate. Inspired by this idea, we developed face-iris system [4] for personal identification at a distance. Some fusion strategies for face and iris [5], [6], [7] have been proposed in the past few years. "
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    ABSTRACT: Most existing face and iris fusion schemes are concerned about improving performance on good quality images under controlled environments. In this paper, we propose a hierarchical fusion scheme for low quality images under uncontrolled situations. In the training stage, canonical correlation analysis (CCA) is adopted to construct a statistical mapping from face to iris in pixel level. In the testing stage, firstly the probe face image is used to obtain a subset of candidate gallery samples via regression between the probe face and gallery irises, then ordinal representation and sparse representation are performed on these candidate samples for iris recognition and face recognition respectively. Finally, score level fusion via min-max normalization is performed to make final decision. Experimental results on our low quality database show the outperforming performance of proposed method.
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