Face recognition using a hybrid model

Abstract

This paper introduces a hybrid face recognition model that combines biologically inspired features and Local Binary Features. The structure of the model is mainly based on the human visual ventral pathway. Previously, object-centered models focus on extracting global view-invariant representation of faces (I. Biederman, 1987) while feed-forward view-based models (HMAX model by Riesenhuber and Poggio, 1999) extract local features of faces by simulating responses of neurons in the human visual system. In this paper we first review the current main face recognition algorithms: Local Binary Pattern model and R&P model. This is followed by a detailed description of their implementation and advantages in overcoming intra-class variance. Results from our model are compared to the original Riesenhuber and Poggio model and Local Binary Pattern model (T. Ahonen et al, 2005). Then the paper will focus on our hybrid biological model which takes advantages of both structural information and biological features. Our model shows improved recognition rates and increased tolerance to intra-personal view differences.