Fusion of PHOG and LDP local descriptors for kernel-based ear biometric recognition

Achieving higher recognition performance in uncontrolled scenarios is a key issue for ear biometric systems. It is almost difficult to generate all discriminative features by using a single feature extraction method. This paper presents an efficient method by combining the two most successful local feature descriptors such as Pyramid Histogram of Oriented Gradients (PHOG) and Local Directional Patterns (LDP) to represent ear images. The PHOG represents spatial shape information and the LDP efficiently encodes local texture information. As the feature sets are curse of high dimension, we used principal component analysis (PCA) to reduce the dimension prior to normalization and fusion. Then, two normalized heterogeneous feature sets are combined to produce single feature vector. Finally, the Kernel Discriminant Analysis (KDA) method is employed to extract nonlinear discriminant features for efficient recognition using a nearest neighbor (NN) classifier. Experiments on three standard datasets IIT Delhi version (I and II) and University of Notre Dame collection E reveal that the proposed method can achieve promising recognition performance in comparison with other existing successful methods.