Multimodal biometrie recognition using human ear and profile face

Abstract

In recent years, many researchers have shown interest in combining features of different biometrie traits to improve recognition performance of the biometrie systems. In this paper, we examine the feature-level fusion of two contactless biometric modalities of the same image i.e. ear and profile face. Initially, two most efficient local feature descriptors such as LDP (Local Directional Patterns) and LPQ (Local Phase Quantisation) are used to represent both biometric modalities. Due to combination of two feature descriptors, dimension of the feature sets are increased and so PCA is separately applied to both modalities before normalization and fusion steps. Finally, to obtain more discriminant nonlinear features the Kernel Dis-criminative Common Vector (KDCV) method is employed after fusion to the combined feature vector. Experimental evaluation on University of Notre Dame (Collection E) side face database clearly reveals the proposed method is more efficient to increase the recognition performance over other existing ear based unimodal and multimodal biometric systems.