Abstract
Local descriptors are widely used technique of feature extraction to obtain information about both local and global properties of an object. Here, we discuss an application of the Chain Code-Based Local Descriptor to face recognition by focusing on various datasets and considering different variants of this description method. We augment the generic form of the descriptor by adding a possibility of grouping pixels into blocks, i.e., effectively describing larger neighborhoods. The results of experiments show the efficiency of the approach. We demonstrate that the obtained results are comparable or even better than those delivered by other important algorithms in the class of methods based on the Bag-of-Visual-Words paradigm.
•An extension of Chain Code-Based Local Descriptor (CCBLD) is proposed.•CCBLD is applied to face recognition task.•Bag-of-Visual-Words paradigm is realized through the dictionary of chain-codes.•Test results show that CCBLD is comparable or outperforms other local descriptors.•The approach is tested using CAS-PEAL, ColorFERET, FG-NET, and other datasets.