计算机应用与软件2016，Vol.33Issue(6)：272-276,5.DOI:10.3969/j.issn.1000-386x.2016.06.065
用于表情识别的半监督学习自适应提升算法
AN ADAPTIVE BOOSTING ALGORITHM WITH SEMI-SUPERVISED LEARNING FOR FACIAL EXPRESSION RECOGNITION
摘要
Abstract
To address the low recognition rate of traditional facial expression recognition algorithm with semi-supervised learning caused by diverse expressions sources and different face attitudes,we propose a novel semi-supervised learning adaptive boosting (SSL-AdaBoost) algorithm based on transplanting learning adaptive boosting algorithm.The algorithm determines the categories of unmarked samples by calculating the marked samples concentrated in training through near neighbour,and recognises by means of AdaBosst.M1 algorithm the facial expression sample with multi-data sources and the facial expression sample of multiple attitudes respectively to realise the multi-category recognition task of samples.Experimental results show that the algorithm significantly improves the expression recognition rate in comparison with the label propagation method and many other semi-supervised learning methods.Besides,it achieves the highest recognition rate by 73.33% on multiple databases and 87.71% on multi-attitude database respectively.关键词
人脸表情识别/半监督学习/自适应提升Key words
Facial expression recognition/Semi-supervised learning/Adaptive boosting分类
信息技术与安全科学引用本文复制引用
吴会丛,贾克斌,蒋斌..用于表情识别的半监督学习自适应提升算法[J].计算机应用与软件,2016,33(6):272-276,5.基金项目
河北省自然科学基金项目（F201420  （）
8113）。 （）
