Semi-Supervised Learning Based Color Iris Recognition
In this paper, we first studied some fusion strategies for color images based iris recognition. The strategies include Min, Max, Sum, Product and Weighted Sum. The fusion takes place at the matching scores level of red, green and blue channels. The experiments were carried out on UBIRIS v1.0. No fusion strategy can guarantee an improvement compared to the red channel, which yields the best single channel performance. We present a semi-supervised learning algorithm for iris recognition. Two methods are employed to estimate the distance between a test image and a subject. Every test image is appended to a class as a new template according to the classification result. The experiments show that, in some scenarios leading to acquisition condition change, the semi-supervised learning method can improve the performance.