Enhancing Iris Recognition
Towards more secure and robust iris recognition systems
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In this book, we propose three techniques to increase the iris recognition robustness and accuracy. First, we propose a new segmentation algorithm to handle iris images were captured on less constrained conditions. This algorithm reduces the error percentage while there are types of noise, such as iris obstructions and specular reflection. The proposed algorithm uses the K-means algorithm, Circular Hough Transform and some new proposed algorithms to detect and isolate noise regions. Second, a study of the effect of the pupil dilation on iris recognition system is performed, in order to show th...
In this book, we propose three techniques to increase the iris recognition robustness and accuracy. First, we propose a new segmentation algorithm to handle iris images were captured on less constrained conditions. This algorithm reduces the error percentage while there are types of noise, such as iris obstructions and specular reflection. The proposed algorithm uses the K-means algorithm, Circular Hough Transform and some new proposed algorithms to detect and isolate noise regions. Second, a study of the effect of the pupil dilation on iris recognition system is performed, in order to show that the pupil dilation degrades iris template and affects the performance of recognition systems. Therefore, a limit of pupil dilation degree is determined. If the degree of pupil dilation exceeds this limit, the iris code will be affected or some of its information will be discarded. This limit can be used to avoid detrimental pupil dilation. Finally, we analyze the iris code bits to determine the consistent and inconsistent bits, and we compare between the inner and outer regions to find which region contains more inconsistent bits.
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