In this work, we propose a novel technique for face recognition with +-90 deg pose variations in image sequences using a cellular simultaneous recurrent network (CSRN). We formulate the recognition problem with such large-pose variations as an implicit temporal prediction task for CSRN. We exploit a face extraction algorithm based on the scale-space method and facial structural knowledge as a preprocessing step. Further, to reduce computational cost, we obtain eigenfaces for a set of image sequences for each person and use these reduced pattern vectors as the input to CSRN. CSRN learns how to associate each face class/person in the training phase. A modified distance metric between successive frames of test and training output pattern vectors indicate either a match or mismatch between the two corresponding face classes. We extensively evaluate our CSRN-based face recognition technique using the publicly available VidTIMIT Audio-Video face dataset. Our simulation shows that for this dataset with large-scale pose variations, we can obtain an overall 77percent face recognition rate.
展开▼
