Single sample face recognition via BoF using multistage KNN collaborative coding
Fan Liua b✉, Sai Yangc✉, Yuhua Dingd, Feng Xua
- aCollege of Computer and Information, Hohai University, Nanjing, China
- bNantong Ocean and Coastal Engineering Research Institute, Hohai University, Nantong, China
- cSchool of Electrical Engineering, Nantong University, Nantong, China
- dSchool of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
- ✉Corresponding author
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Abstract
In this paper, we propose a multistage KNN collaborative coding based Bag-of-Feature (MKCC-BoF) method to address SSPP problem, which tries to weaken the semantic gap between facial features and facial identification. First, local descriptors are extracted from the single training face images and a visual dictionary is obtained offline by clustering a large set of descriptors with K-means. Then, we design a multistage KNN collaborative coding scheme to project local features into the semantic space, which is much more efficient than the most commonly used non-negative sparse coding algorithm in face recognition. To describe the spatial information as well as reduce the feature dimension, the encoded features are then pooled on spatial pyramid cells by max-pooling, which generates a histogram of visual words to represent a face image. Finally, a SVM classifier based on linear kernel is trained with the concatenated features from pooling results. Experimental results on three public face databases show that the proposed MKCC-BoF is much superior to those specially designed methods for SSPP problem. Moreover, it also has great robustness to expression, illumination, occlusion and, time variation.