Unconstrained Face Verification Based on Monogenic Binary Pattern and Convolutional Neural Network

Abstract

Unconstrained Face Verification is still an important problem worth researching. The major challenges such as illumination, pose, occlusion and expression can produce more complex variations in both shape and texture of the face. In this paper, we propose a method based on Monogenic Binary Pattern and Convolutional Neural Network (MBP-CNN) to improve the performance of face recognition system. For each facial image, the proposed method firstly extracts local features using Monogenic Binary Pattern (MBP) which is an excellent and powerful local descriptor compared to the well-recognized Gabor filtering-based LBP models. Then, we use Convolutional Neural Networks which is one of the best representative network architectures of deep learning in the literature, in order to extract more deep features. Thus, the developed MBP-CNN has robustness to variations of illumination, occlusion, pose, expression, texture and shape by combining Monogenic Binary Pattern and convolutional neural network. Moreover, MBP-CNN was more accurately represented by combining global and local information of facial images. Experiments demonstrate that our method provided competitive performance on the LFW database, compared to the others described in the state-of-the-art.