Abstract：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 multi-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.
2013, Vol. 26 
