Iris recognition is biometrics technology with high accuracy and strong stability. Iris recognition algorithms are becoming mature, but the bottleneck of iris recognition is that iris image acquisition is not very convenient and users need actively cooperate with the machine to perform the recognition. For the purpose of enhancing the usability of iris recognition, we begin the study of iris recognition at a distance and try to complement non-cooperative iris recognition at 2 or 3 meters. We investigate key points involving the iris recognition at a distance such as image acquisition, human-machine interface, image sequence processing and iris feature matching. The main contributions include: (1) Implement the iris image acquisition at a distance. It includes iris texture imaging, optical design, sensor selection, illuminator safety and so on. Finally we design an iris image acquisition system and establish the image database of iris recognition at a distance. (2) Realize the active human-computer-interaction at a distance. We propose three active human-computer-interaction methods: the active tracking method based on the stereo vision and pan-tilt-zoom camera, the active tracking method based on a single wide-angle camera and pan-tilt-zoom camera, and the image acquisition method based on the camera array. With those methods, the capturing range and depth of the iris recognition system is enlarged and the system can actively cooperate with different heights and positions of users at a distance. (3) Iris image sequence processing. Since the high-resolution camera is used in the iris recognition system at a distance, we develop special image processing method to select clear iris images from such high-pixel real-time sequence. Moreover, we propose two new strategies for iris recognition. One is of quality-based dynamic accept threshold for iris matching, and the other is of quality-based adaptive iris-face fusion. Those strategies make more low-quality iris image recognized, so greatly improve the recognition rate of the sequence. (4) Improve the method of iris feature representation and iris matching. We propose an iris matching strategy based on the personalized weight map. This weight map is generated from multiple registered images and reflects the different stability of different iris regions. By calculating the iris matching score based on optimized weight map, this method greatly improves the classification performance, especially for those l...
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