Mixed 2D-3D information for face recognition
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Recent face recognition algorithm can achieve high accuracy when testing face samples are frontal. However, when face pose changes largely, the performance of existing methods drop drastically. In this paper, we propose an improved algorithm aiming at recognizing faces of different poses when each face class has only one frontal training sample. For each sample, a 3D face is constructed by using 3D Morphable Model (3DMM). The shape and texture parameters of 3DMM are recovering by fitting the model to the 2D face sample which is a non-linear optimization problem. The virtual faces of different views are generated from the 3DMM to assist face recognition. Different from the conventional optimization energy function, proposed energy function takes not only image intensity but also shape constraint into account. In this paper, we locate 88 sparse points from the 2D face sample by automatic face fitting and use their correspondence in the 3D face as shape constraint. We experiment proposed method on the publicly available CMUPIE database which includes faces viewed from 11 different poses and the results show that proposed method is effective and the face recognition results towards pose-variant are promising.