Feature extraction is the crucial part of an iris recognition system. Multi-resolution analysis such as Gabor filters and wavelet transform (WT) help to characterize different scales of iris texture. In this paper, we propose a novel multi-resolution approach based on wavelet packets transform (WPT) for iris texture analysis and recognition. The development of this approach is motivated by the observation that dominant frequencies of iris texture are located in the low and middle frequency channels. With an adaptive threshold, we quantize WPT subimages coefficients into 1, 0 or -1 as iris signature. This signature presents the local information of different irises. Manhattan Distance is used to measure the degree of dissimilarity between two iris sets. Experimental results show that the algorithm is efficient to describe local information
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