3D Shape and Texture Features Fusion using Auto-Encoder for Efficient Face Recognition

Face recognition is one of the most widely used biometrics for identifying people. However, face images suffer from several issues that could affect the achieved results, especially in a crowded environment. Such as facial expression, occlusion, low resolution, noise, illumination and pose variation. In this paper, we propose a robust image representation system for face recognition. First, 3D face data reconstructed from 2D images are used instead of 3D capture. This is accomplished by modeling the difference in the texture map of the 3D aligned input and reference images. Then, fusing shape and texture local binary patterns (LBP) on a mesh for face recognition using the Mesh-LBP. Finally, we used a deep Auto-Encoder to create a compact data representation based on the obtained face images descriptors from the Mesh-LBP. Through experiments conducted on the Multi-PIE and Bosphorus databases, we show that our method is very competitive against state-of-the-art methods.