CAJ | 학술논문

提出了一种基于随机块特征和自适应词典学习的人脸表情识别方法。利用Haar-like特征和人脸几何结构信息可靠定位眼睛和嘴巴；在眼睛和嘴巴附近抽取随机块，构建特征矢量；将特征矢量进行词典学习，得到表情词典；根据待测表情在表情词典上的稀疏分解对表情进行分类。在JAFFE和Cohn-kanade表情库中进行了对比实验，结果表明该方法具有较好的识别性能，对噪声和遮挡具有较好的鲁棒性。
제출료일충기우수궤괴특정화자괄응사전학습적인검표정식별방법。이용Haar-like특정화인검궤하결구신식가고정위안정화취파；재안정화취파부근추취수궤괴，구건특정시량；장특정시량진행사전학습，득도표정사전；근거대측표정재표정사전상적희소분해대표정진행분류。재JAFFE화Cohn-kanade표정고중진행료대비실험，결과표명해방법구유교호적식별성능，대조성화차당구유교호적로봉성。
A facial expression recognition method based on adaptive dictionary learning and sparse representation was proposed.Eyes and mouth were located by Haar-like features and the geometric structure of face.For learning dictiona-ries, random blocks near facial salient points were extracted, and those blocks were trained from the same kind of ex-pression to form one expression dictionary.Then, all expression dictionaries were arranged into a learned dictionary. The test expressions were classified by sparse decomposition.The good performance was verified by the experiments on standard JAFFE and Cohn-kanade expression database.Good robustness was obtained even if the image had noise cor-ruption or under occlusion.