Local binary LDA for face recognition

Abstract

Extracting discriminatory features from images is a crucial task for biometric recognition. For this reason, we have developed a new method for the extraction of features from images that we have called local binary linear discriminant analysis (LBLDA), which combines the good characteristics of both LDA and local feature extraction methods. We demonstrated that binarizing the feature vector obtained by LBLDA significantly improves the recognition accuracy. The experimental results demonstrate the feasibility of the method for face recognition as follows: on XM2VTS face image database, a recognition accuracy of 96.44% is obtained using LBLDA, which is an improvement over LDA (94.41%). LBLDA can also outperform LDA in terms of computation speed. © 2011 Springer-Verlag.