Pose-invariant face recognition is a practically useful but challenging task. This paper introduces a novel method to learn pose-invariant feature representation without normalizing profile faces to frontal ones or learning disentangled features. We first design a novel strategy to learn pose-invariant feature embeddings by distilling the angular knowledge of frontal faces extracted by teacher network to student network, which enables the handling of faces with large pose variations. In this way, the features of faces across variant poses can cluster compactly for the same person to create a pose-invariant face representation. Secondly, we propose a Pose-Adaptive Angular Distillation loss to mitigate the negative effect of uneven distribution of face poses in the training dataset to pay more attention to the samples with large pose variations. Extensive experiments on two challenging benchmarks (IJB-A and CFP-FP) show that our approach consistently outperforms the existing methods.

Although face recognition has been widely applied in many areas, pose-robust face recognition is still a challenging topic due to the large pose variations in real scenes. In this paper, we propose to learn the pose-robust face representation by normalizing the profile face in feature level directly and jointly considering both intra-class compactness and inter-class separability. Our approach minimizes the angular distance between the profile face and the positive frontal anchor. And it maximizes the angular distance between the profile face and the negative frontal anchor simultaneously. Furthermore, we modify the Triplet loss and derive the Triplet Angular loss to guarantee the intra-class compactness and the inter-class separability in angular space. In this way, the faces under varying poses can cluster compactly to create a pose-robust feature representation. Extensive experiments on two challenging benchmarks (CFP-FP and IJB-A) illustrate that our approach achieves a competitive performance in the field of pose-robust face recognition.

In deep face recognition, the commonly used softmax loss and its newly proposed variations are not yet sufficiently effective to handle the class imbalance and softmax saturation issues during the training process while extracting discriminative features. In this brief, to address both issues, we propose a class-variant margin (CVM) normalized softmax loss, by introducing a true-class margin and a false-class margin into the cosine space of the angle between the feature vector and the class-weight vector. The true-class margin alleviates the class imbalance problem, and the false-class margin postpones the early individual saturation of softmax. With negligible computational complexity increment during training, the new loss function is easy to implement in the common deep learning frameworks. Comprehensive experiments on the LFW, YTF, and MegaFace protocols demonstrate the effectiveness of the proposed CVM loss function.