۱- Introduction

۲- Related works

۳- Method

۴- Experiments

۵- Conclusions

References

Abstract

Unconstrained face recognition still remains a challenging task due to various factors such as pose, expression, illumination, partial occlusion, etc. In particular, the most significant appearance variations are stemmed from poses which leads to severe performance degeneration. In this paper, we propose a novel Deformable Face Net (DFN) to handle the pose variations for face recognition. The deformable convolution module attempts to simultaneously learn face recognition oriented alignment and identity-preserving feature extraction. The displacement consistency loss (DCL) is proposed as a regularization term to enforce the learnt displacement fields for aligning faces to be locally consistent both in the orientation and amplitude since faces possess strong structure. Moreover, the identity consistency loss (ICL) and the pose-triplet loss (PTL) are designed to minimize the intra-class feature variation caused by different poses and maximize the inter-class feature distance under the same poses. The proposed DFN can effectively handle pose invariant face recognition (PIFR). Extensive experiments show that the proposed DFN outperforms the state-of-the-art methods, especially on the datasets with large poses.

INTRODUCTION

Face recognition, as a fundamental problem in computer vision, has received more and more attentions in recent years. Equipped with powerful convolutional neural networks (CNNs), the accuracy has a rapid boost that face recognition under controlled settings (i.e., near-frontal poses, neutral expressions, normal illuminations, etc.) seems to be solved. However, under the uncontrolled environment, a number of factors (e.g., pose, illumination, resolution, occlusion, and expression) significantly affect the performance of face recognition system. Among these factors, self-occlusion from out-plane poses brings about large appearance variations. The misalignment problem heavily hurts the face recognition system. In this paper, we further push the frontier of this research area by simultaneously considering face recognition oriented alignment and identitypreserving feature extraction under deep neural networks, which aims at tackling the pose-invariant face recognition (PIFR) problem.