文献类型：期刊文献

英文题名：An efficient unconstrained facial expression recognition algorithm based on Stack Binarized Auto-encoders and Binarized Neural Networks

作者：Sun, Wenyun[1];Zhao, Haitao[2];Jin, Zhong[1]

机构：[1]Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China;[2]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai, Peoples R China

年份：2017

卷号：267

起止页码：385

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20172703898441);WOS:【SCI-EXPANDED(收录号:WOS:000409285400034)】；

基金：This work is partially supported by National Natural Science Foundation of China under Grant Nos. 61375007, 61373063, 61233011, 91420201, 61472187 and by National Basic Research Program of China under Grant No. 2014CB349303.

语种：英文

外文关键词：Facial expression recognition; Unconstrained face; Binarized Auto-encoder

摘要：Although deep learning has achieved good performances in many pattern recognition tasks, the over fitting problem is still a serious issue for training deep networks containing large sets of parameters with limited labeled data. In this work, Binarized Auto-encoders (BAEs) and Stacked Binarized Auto-encoders (Stacked BAEs) are proposed to learn a kind of domain knowledge from a large-scale unlabeled facial dataset. By transferring the knowledge to another Binarized Neural Networks (BNNs) based supervised learning task with limited labeled data, the performance of the BNNs can be improved. A real-world facial expression recognition system is constructed by combining an unconstrained face normalization method, a variant of LBP descriptor, BAEs and BNNs. The experiment result shows that the whole system achieves good performance on the Static Facial Expressions in the Wild (SFEW) benchmark with minimal hardware requirements and lower memory and computation costs. (C) 2017 Elsevier B.V. All rights reserved.