Chapter 1 - Iris feature extraction using three-level Haar wavelet transform and modified local binary pattern

Abstract

In this chapter, a novel feature extraction method is proposed for faster iris recognition. This new method is a hybrid process combining three-level Haar wavelet transform (HWT) and modified local binary pattern (MLBP). In this hybrid method, firstly, HWT is applied to the normalized iris image, resulting in four output images including the approximation image known as LL subband. This LL subband is then further decomposed using HWT into four subimages. The resultant second-level LL is decomposed using HWT into the third-level LL subband. The application of repeated HWT extracts the major information-containing region, reducing the information size. Next, MLBP is applied to the obtained LL, where MLBP includes local binary pattern and Exclusive OR operations. The output of MLBP is a binary iris template. The effectiveness of this proposed hybrid HWT–MLBP method is experimentally evaluated using three different datasets, namely CASIA-IRIS-V4, CASIA-IRIS-V1, and MMU. The proposed HWT–MLBP method can obtain a reduced feature vector length of 1×64. For instance, when applied to the CASIA-IRIS-V1 dataset, HWT–MLBP can obtain an average correct recognition rate of 98.30% and a false acceptance rate of 0.003%. Results indicate that the proposed HWT–MLBP outperforms existing methods in terms of reduced feature length, which ensures faster iris recognition.