基于特征优选和字典优化的组稀疏表示表情识别

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.

Group Sparse Representation Based on Feature Selection and Dictionary Optimization for Expression Recognition

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.