| Summary: | 碩士 === 國立暨南國際大學 === 電機工程學系 === 97 === In biometric of iris recognition technology, the spatial patterns have been studied and recognized electively for several years. The iris recognition researches most attention on gray-level image. This paper delves into the color distribution of iris recognition has rich information enough to authenticate personal identity. This paper delved into the problem of whether iris color has enough information to verify personal identity or not.
This paper proposed an iris recognition system that tested on the UBIRIS database which includes 1205 images from 241 classes. The iris recognition system consists of iris localization, feature extraction, and pattern matching. In this paper, we investigate some color features information described by the following three forms: structure, space and proportion. The iris recognition experiments utilize different image color descriptors, such as texture, gray, RGB, YIQ, YCbCr, CIELab, and other quantization of color space. In order to observe the structure features, we adopt 2D co-occurrence matrix (2D-CM) and 3D co-occurrence matrix (3D-CM). The analysis of space features uses K-means algorithm and centriod splitting algorithm to find cluster centers. The analysis of proportion features, we carry out the experiments extensively with a number of feature extraction methods including color moment, color probabilities distributions module (CPDM), two-dimensional principal components analysis (2D-PCA), two-dimensional linear discriminate analysis (2D-LDA), K-means Histogram, three-dimensional color level co-occurrence matrix (3D-CLCM), motif co-occurrence matrix (MCM), and we proposed of local color proportion descriptor (LCPD) that encode combined statistic measure with Peano scanning. According to the analysis and comparison, a significant summary is given. The feature of color information is useful and uniquely.
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