Learning Identity-Aware Face Features Across Poses Based on Deep Siamese Networks

Face recognition is an important biometric due to its non-intrusive collection of data that can be applied to surveillance systems. However, the human pose is unconstrained under surveillance, and there is only one frontal face available in the gallery in most scenarios, and these two factors challenge face recognition performance. A goodly amount of research has been published to solve this problem. Specially, deep learning-based methods learn generic models between poses to synthesize different poses of a given face, however, generic synthesis models can lose the face identity while warping the face, which deteriorates the discriminative capability of learned features. In this paper, we proposed the deep Siamese networks to learn identity-aware and pose-invariant features, adding contrastive loss to the face synthesis model to preserve the face identity while synthesizing the face. In addition, we trained various face synthesis models with different target poses as supervisory signals, the learned pose-invariant features were incorporated by another Siamese network, resulting in deeper pose-invariant and identity-aware features. The proposed network is free of landmark estimation and face pose, and it is in real time. We tested the proposed algorithm in the FERET and CAS-PEAL datasets, and experimental results demonstrated that our network achieved superior performance to that of recently published algorithms for cross-pose face recognition, especially the 2D deep learning-based algorithms.