Multimodal biometrics system based on face profile and ear

Abstract

Face recognition from a side profile view, has recently received significant attention in the literature. Even though current face recognition systems have reached a certain level of maturity at angles up to 30 degrees, their success is still limited with side profile angles. This paper presents an efficient technique for the fusion of face profile and ear biometrics. We propose to use a Block-based Local Binary Pattern (LBP) to generate the features for recognition from face profile images and ear images. These feature distributions are then fused at the score level using simple mean rule. Experimental results show that the proposed multimodal system can achieve 97:98% recognition performance, compared to unimodal biometrics of face profile 96.76%, and unimodal biometrics of ear 96.95%, details in the Experimental Results Section. Comparisons with other multimodal systems used in the literature, like Principal Component Analysis (PCA), Full-space Linear Discriminant Analysis (FSLDA) and Kernel Fisher discriminant analysis (KFDA), are presented in the Experimental Results Section.