基于多分离部件稀疏编码的人脸图像分析

Face Image Analysis Based on Multiple Separated Component Sparse Coding

考虑到不同部件(眼睛,嘴等)对人脸分析的贡献差别,提出基于多部件稀疏编码的人脸图像分析方法.首先,选取对人脸(表情)分析影响较大的几个人脸部件,然后,利用多视角稀疏编码方法学习各部件的字典,并计算相应的稀疏编码,最后,将稀疏编码输入分类器(支持向量机和最小均方误差)进行判决.分别在数据库JAFFE和Yale上进行人脸(表情)识别及有遮挡的人脸(表情)识别实验.实验结果表明,基于多部件稀疏编码的人脸分析能较好地调节各部件的权重,优于各单一部件和简单的多部件融合方法的性能.

Considering the different contributions of different facial components to face analysis, e.g. eyes, mouth etc. , a face analysis based on multi-component sparse coding is proposed. Firstly, some facial components which play important role to face analysis are selected. Then, the dictionaries of multiple components are learnt by using muhi-view sparse coding algorithm, and the sparse codes of each face image are computed based on the dictionary. The final decision is made through pooling the sparse codes into support vector machines and least squares classifiers. Face analysis experiments include face recognition, facial expression recognition, face recognition with occlusion, and facial expression recognition with occlusion. The experimental results show that the proposed method based on multi-component sparse coding learns optimal weights of different facial components and outperforms single facial component method and simple multi-component fusion method.