摘要：Facial recognition is a challenging area of research due to difficulties with robust face recognition (FR) under occlusion and sparse representation-based classification (SRC) only focusing on face global features. To solve these issues, we proposed an occluded FR method based on dictionary learning for sparse representation and sub-classifiers fusion (LSSRC), which efficiently combines local and overall characteristics of face images. First, we partitioned continuous but non-lapped blocks of the face by multi-resolution blocking. Then, for each block, SRC was used for feature extraction and face classification. We established a sub-block dictionary and conducted K-SVD dictionary learning, established sub-classifiers and determined weight. Finally, we conducted sub-classifier fusion recognition using voting rules with weight. Results using the AR and YaleA database showed that our algorithm achieved superior recognition performance to the existing sparse representation classification occluded FR method.
