Iris feature extraction and matching based on multiscale and directional image representation

This paper presents a new filterbank-based iris recognition method that effectively extracts the spatial and directional features of iris patterns on multiple scales, then performs matching. First, the proposed method localizes the iris area from an input image and establishes a region of interest (ROI) for feature extraction. Second, the iris features are extracted on multiple scales from the ROI and a feature vector generated using a band pass filter and directional filter bank (DFB), which decomposes the image into several directional subband outputs. Finally, iris pattern matching robust to various rotations of the input is performed based on finding the Hamming distance between the corresponding feature vectors. Experimental results demonstrate that the proposed method is both effective in extracting directional and multi-resolutional features from iris patterns and robust to input image rotation due to head tilt.