An Effective Feature Descriptor with Gabor Filter and Uniform Local Binary Pattern Transcoding for Iris Recognition

Abstract

Iris recognition is recognized as one of the most reliable and efficient technique for human identification in the biometric fields. The Gabor filter and local binary pattern (LBP) are widely adopted for feature extraction in face recognition. However, it is difficult to achieve high recognition accuracy when the Gabor filter or LBP is directly applied to iris texture representation. This paper presents an effective iris feature descriptor, which first uses 2D-Gabor filter to extract multi-orientation imaginary (MOI) feature, and then applies uniform LBP for region feature encoding. Thus, the MOI feature-by-point energy is converted into that of the uniform LBP histogram-by-block, during which the distributions of the intra- and inter-class are greatly widened. Such process largely improves distinguishability of MOI features. Finally, the Bhattacharyya distance is adopted for matching. Experimental results on CASIA and JLU iris image databases show that this method performs better for combining MOI features and LBP encoding as compared to their individual function.