基于特征优选和字典优化的组稀疏表示表情识别

doi:10.16451/j.cnki.issn1003-6059.202105007

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摘要针对在小样本人脸表情数据库上识别模型过拟合问题,文中提出基于特征优选和字典优化的组稀疏表示分类方法.首先提出特征优选准则,选择相同类级稀疏模式、不同类内稀疏模式的互补特征构建字典.然后对字典进行最大散度差优化学习,使字典在不失真重构特征的同时具有较高鉴别能力.最后联合优化后的字典进行组稀疏表示分类.在JAFFE、CK+数据库上的实验表明,文中方法对样本减少具有鲁棒性,泛化能力较强,识别精度较优.

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	谢惠华
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	王艳
	陈昊

关键词 ：小样本表情识别, 特征优选, 最大散度差优化学习, 组稀疏表示

Abstract：To solve the over-fitting problem of recognition model on small sample facial expression database, a group sparse representation classification method based on feature selection and dictionary optimization is put forward. Firstly, the feature selection criterion is proposed, and the complementary features of same class-level sparse mode and different intra-class sparse mode are selected to build a dictionary. Then, the dictionary is learned by maximum scatter difference optimization to reconstruct features without distortion and acquire a high discriminative ability. Finally, the optimized dictionary is combined for group sparse representation classification. Experiments on JAFFE and CK+ databases show that the proposed method is robust to sample reduction with high generalization ability and recognition accuracy.

Key words： Small Sample Expression Recognition Feature Selection Maximum Scatter Difference Optimization Learning Group Sparse Representation

收稿日期: 2020-12-13

ZTFLH:

TP 391

基金资助:国家自然科学基金项目(No.61866025,61772255,61440049)、江西省教育厅科学技术项目(No.GJJ170608)、江西省研究生创新专项资金项目(No.YC2019-S339)、江西省图像处理与模式识别重点实验室开放基金项目(No.ET201604246)

通讯作者: 黎明,博士,教授,主要研究方向为图像处理、模式识别、高维多目标优化问题.E-mail:liming@nchu.edu.cn.

作者简介: 谢惠华,硕士研究生,主要研究方向为图像处理、模式识别.E-mail:xiehuihua97@126.com.王艳,博士,讲师,主要研究方向为图像处理、模式识别.E-mail:vicie414@126.com.陈昊,博士,副教授,主要研究方向为进化算法、图像处理、模式识别等.E-mail:chenhaoshl@nchu.edu.cn.

引用本文:

谢惠华, 黎明, 王艳, 陈昊. 基于特征优选和字典优化的组稀疏表示表情识别[J]. 模式识别与人工智能, 2021, 34(5): 446-454. XIE Huihua, LI Ming, WANG Yan, CHEN Hao. Group Sparse Representation Based on Feature Selection and Dictionary Optimization for Expression Recognition. , 2021, 34(5): 446-454.

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