Abstract
Sparse representation-based classification (SRC) method has demonstrated promising results in face recognition (FR). In this paper, we consider the problem of face recognition with occlusion. In sparse representation-based classification method, the reconstruction residual of test sample over the training set is usually heterogeneous with the training samples, highlighting the occlusion part in test sample. We detect the occlusion part by extracting a mask from the reconstruction residual through threshold operation. The mask will be applied in the representation-based classification framework to eliminate the impact of occlusion in FR. The method does not assume any prior knowledge about the occlusion, and extensive experiments on publicly available databases show the efficacy of the method.
Highlights
Face recognition (FR) is one of the most intensively investigated topics in biometrics
We present a novel method for robust face recognition in the presence of occlusion, such as block occlusion, pixel occlusion and real disguise
We exploit the reconstruction residual of collaborative representation based classification (CRC) model to detect the occlusion in test sample, and get a binary mask of the occlusion
Summary
Face recognition (FR) is one of the most intensively investigated topics in biometrics. Classical dimensionality reduction methods include Principle Component Analysis (PCA) [2] and Linear Discriminative Analysis (LDA) [3]. Occlusion poses a significant obstacle in a robust, real-world face recognition system [5,6]. This difficulty is mainly due to the error in query sample incurred by occlusion. We present a novel method for robust face recognition in the presence of occlusion, such as block occlusion, pixel occlusion and real disguise.
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