A Fast and General Method for Partial Face Recognition

Recently, holistic face recognition technology has been increasingly mature. However, as for many unconstrained environments, the captured face image is more likely not holistic, but partial discriminative face area. To address this, we propose a fast and general method for partial face recognition. There, our method needn't alignment by fiducial points or cropping to the same size, for all facial images in the gallery set and probe set. In other words, we use the initial captured faces as input. Besides, our method can deal with single sample face recognition problem. For a pair of gallery image and probe image, firstly we detect key-points as well as extracting their local descriptors. Specially, in order to improve robustness of descriptors, we exploit gradient orientation modification and L2 normalization. Then, we use sparse representation based on multi-descriptors to recognize probe image. Experimental results on public face datasets demonstrate the effectiveness of the proposed method.