Abstract
Sparse representation based on classification and collaborative representation based classification with regularized least square has been successfully used in face recognition. The over-completed dictionary is crucial for the approaches based on sparse representation or collaborative representation because it directly determines recognition accuracy and recognition time. In this paper, we proposed an algorithm of adaptive dictionary learning according to the inputting testing image. First, nearest neighbors of the testing image are labeled in local configuration pattern (LCP) subspace employing statistical similarity and configuration similarity defined in this paper. Then the face images labeled as nearest neighbors are used as atoms to build the adaptive representation dictionary, which means all atoms of this dictionary are nearest neighbors and they are more similar to the testing image in structure. Finally, the testing image is collaboratively represented and classified class by class with this proposed adaptive over-completed compact dictionary. Nearest neighbors are labeled by local binary pattern and microscopic feature in the very low dimension LCP subspace, so the labeling is very fast. The number of nearest neighbors is changeable for the different testing samples and is much less than that of all training samples generally, which significantly reduces the computational cost. In addition, atoms of this proposed dictionary are these high dimension face image vectors but not lower dimension LCP feature vectors, which ensures not only that the information included in face image is not lost but also that the atoms are more similar to the testing image in structure, which greatly increases the recognition accuracy. We also use the Fisher ratio to assess the robustness of this proposed dictionary. The extensive experiments on representative face databases with variations of lighting, expression, pose, and occlusion demonstrate that the proposed approach is superior both in recognition time and in accuracy.
Highlights
IntroductionThe human face has been paid more attention because it can be captured with a common camera even without cooperation of the subject
As a biological feature, the human face has been paid more attention because it can be captured with a common camera even without cooperation of the subject
This proposed adaptive dictionary is different for different testing images, so different sets of training samples have a little influence on experimental results
Summary
The human face has been paid more attention because it can be captured with a common camera even without cooperation of the subject. The performance of face recognition is affected by the expression, illumination, occlusion, pose, age change, and so on. There are still some challenges in the field of unrestricted face recognition. Facial images are very high dimensional, which is bad for classification. Dimension reduction is carried out before classification. Principle component analysis (PCA) [1,2,3,4] has become the classic reducing dimension approach and has been used widely in image processing and pattern recognition fields. All training images make up covariance matrix, and these eigenvectors corresponding to the bigger eigenvalues of covariance matrix span the linear feature subspace. Through projecting high-dimensional original face images onto the low dimensional linear feature subspace, PCA performs the dimension reduction and preserves the global structure of an image
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