Multi-pose face reconstruction and recognition based on multi-task learning
OUYANG Ning1,2, MA Yutao2, LIN Leping1,2
1. Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education(Guilin University of Electronic Technology), Guilin Guangxi 541004, China; 2. School of Information and Communication, Guilin University of Electronic Technology, Guilin Guangxi 541004, China
Abstract：To circumvent the influence of pose variance on face recognition performance and considerable probability of losing the facial local detail information in the process of pose recovery, a multi-pose face reconstruction and recognition method based on multi-task learning was proposed, namely Multi-task Learning Stacked Auto-encoder (MtLSAE). Considering the correlation between pose recovery and retaining local detail information, multi-task learning mechanism was used and sparse auto-encoder with non-negativity constraints was introduced by MtLSAE to learn part features of the face when recovering frontal images using step-wise approach. And then the whole net framework was learned by sharing parameters between above two related tasks. Finally, Fisherface was used for dimensionality reduction and extracting discriminative features of reconstructed positive face image, and the nearest neighbor classifier was used for recognition. The experimental results demonstrate that MtLSAE achieves good pose reconstruction quality and makes facial local texture information clear; on the other hand, it also achieves higher recognition rate than some classical methods such as Local Gabor Binary Pattern(LGBP), View-Based Active Appearance (VAAM) and Stacked Progressive Auto-encoder (SPAE).
欧阳宁, 马玉涛, 林乐平. 基于多任务学习的多姿态人脸重建与识别[J]. 计算机应用, 2017, 37(3): 896-900.
OUYANG Ning, MA Yutao, LIN Leping. Multi-pose face reconstruction and recognition based on multi-task learning. Journal of Computer Applications, 2017, 37(3): 896-900.
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2017, Vol. 37 
