Recognizing face profiles in the presence of hairs/glasses interferences

Facial profile provides a complementary structure of the face that is not present in frontal faces, which has been used in personal identification, face perception research and 3D face construction. In this paper, we present a novel local attributed string matching (LAStrM) approach to recognize face profiles in the presence of interferences. The conventional profile recognition algorithms heavily depend on the accuracy of the facial area cropping. However, in realistic scenarios the facial area may be difficult to localize due to interferences (e.g., glasses, hairstyles). The proposed approach is able to efficiently find the most discriminative local parts between face profiles addressing the recognition problem with interferences. Experimental results have shown that the proposed matching scheme is robust to interferences compared against several primary approaches using two profile image databases (Bern and FERET). It has potential capability for partially occluded shape classification.