title: Large-scale pose-invariant face recognition using cellular simultaneous recurrent network.
abstract: In this work, we propose a novel technique for face recognition with +/-90 degrees 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 77% face recognition rate.
authors.affiliation: Intelligent Systems and Image Processing Lab, Department of Electrical and Computer Engineering, University of Memphis, Memphis, Tennessee 38152, USA.
authors.forename: Yong
authors.initials: Y
authors.lastname: Ren
authors.forename: Khan M
authors.initials: KM
authors.lastname: Iftekharuddin
authors.forename: William E
authors.initials: WE
authors.lastname: White