Local feature hierarchy for face recognition across pose and illumination

TL;DR

This paper introduces an end-to-end convolutional network that extracts pose and illumination invariant local features for face recognition, significantly improving accuracy across challenging conditions.

Contribution

The paper proposes a multi-stream multi-layer 1x1 convolutional network to extract hierarchical local features for robust face recognition under pose and illumination variations.

Findings

Achieved 96.9% recognition rate on multiPIE dataset, surpassing previous methods.

Improved recognition accuracy by 7.5% over state-of-the-art.

Reaching 97.8% accuracy for profile views, a 19% improvement.

Abstract

Even though face recognition in frontal view and normal lighting condition works very well, the performance degenerates sharply in extreme conditions. Recently there are many work dealing with pose and illumination problems, respectively. However both the lighting and pose variation will always be encountered at the same time. Accordingly we propose an end-to-end face recognition method to deal with pose and illumination simultaneously based on convolutional networks where the discriminative nonlinear features that are invariant to pose and illumination are extracted. Normally the global structure for images taken in different views is quite diverse. Therefore we propose to use the 1*1 convolutional kernel to extract the local features. Furthermore the parallel multi-stream multi-layer 1*1 convolution network is developed to extract multi-hierarchy features. In the experiments we obtained the average face recognition rate of 96.9% on multiPIE dataset,which improves the state-of-the-art of face recognition across poses and illumination by 7.5%. Especially for profile-wise positions, the average recognition rate of our proposed network is 97.8%, which increases the state-of-the-art recognition rate by 19%.