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摘要
In order to deal with facial occlusion effectively, the authors propose a powerful but simple face representation method, called adaptive Weberfaces (AdapWeber), based on human visual perception change model and the Weber ratio R implied in Weber's law. Specifically, human perception is naturally highly selective and robust to occlusions, and the Weber ratio R is very important to enhance feature redundancy. As feature redundancy and locality are two guiding principles against facial occlusion, they further develop eight variants of AdapWeber, collectively referred to as single-scale and single-orientation (SSSO) AdapWeber, by shrinking the kernel locality and varying the kernel orientation of the original AdapWeber, and integrate them to formulate a multi-scale and multi-orientation (MSMO) AdapWeber. A natural by-product of MSMO AdapWeber is MSMO Weberfaces. Experiments on four benchmark databases, including Extended Yale B, AR, UMB-DB, and LFW, showed that MSMO AdapWeber/Weberfaces, rather than any variant of SSSO AdapWeber/Weberfaces, outperformed several popular feature extraction approaches in many scenarios, especially when the occlusion level is very high or the image dimension is very low. This result demonstrates that several occlusion-weak features can be combined together to construct an occlusion-robust feature.
