Open Access

View Influence Analysis and Optimization for Multiview Face Recognition

EURASIP Journal on Image and Video Processing20072007:025409

DOI: 10.1155/2007/25409

Received: 1 May 2006

Accepted: 24 June 2007

Published: 23 August 2007


We present a novel method to recognize a multiview face (i.e., to recognize a face under different views) through optimization of multiple single-view face recognitions. Many current face descriptors show quite satisfactory results to recognize identity of people with given limited view (especially for the frontal view), but the full view of the human head has not yet been recognizable with commercially acceptable accuracy. As there are various single-view recognition techniques already developed for very high success rate, for instance, MPEG-7 advanced face recognizer, we propose a new paradigm to facilitate multiview face recognition, not through a multiview face recognizer, but through multiple single-view recognizers. To retrieve faces in any view from a registered descriptor, we need to give corresponding view information to the descriptor. As the descriptor needs to provide any requested view in 3D space, we refer to it as "3D" information that it needs to contain. Our analysis in various angled views checks the extent of each view influence and it provides a way to recognize a face through optimized integration of single view descriptors covering the view plane of horizontal rotation from −90 to 90 and vertical rotation from −30 to 30. The resulting face descriptor based on multiple representative views, which is of compact size, shows reasonable face recognition performance on any view. Hence, our face descriptor contains quite enough 3D information of a person's face to help for recognition and eventually for search, retrieval, and browsing of photographs, videos, and 3D-facial model databases.


Authors’ Affiliations

School of Information Technology and Engineering, University of Ottawa
Computer Science Department, Carnegie Mellon University


  1. Samal A, Iyengar PA: Automatic recognition and analysis of human faces and facial expressions: a survey. Pattern Recognition 1992,25(1):65-77. 10.1016/0031-3203(92)90007-6View ArticleGoogle Scholar
  2. Li SZ, Zhu L, Zhang ZQ, Blake A, Zhang HJ, Shum H: Statistical learning of multi-view face detection. Proceedings of the 7th European Conference on Computer Vision (ECCV '02), May 2002, Copenhagen, Denmark 4: 67-81.Google Scholar
  3. Li Y, Gong S, Liddell H: Support vector regression and classification based multi-view facedetection and recognition. Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition, March 2000, Grenoble, France 300-305.Google Scholar
  4. Shakhnarovich G, Lee L, Darrell T: Integrated face and gait recognition from multiple views. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '01), December 2001, Kauai, Hawaii, USA 1: 439-446.Google Scholar
  5. Blanz V, Vetter T: Face recognition based on fitting a 3D morphable model. IEEE Transactions on Pattern Analysis and Machine Intelligence 2003,25(9):1063-1074. 10.1109/TPAMI.2003.1227983View ArticleGoogle Scholar
  6. Bronstein AM, Bronstein MM, Kimmel R: Expression-invariant 3D face recognition. Proceedings of the 4th International Conference on Audio- and Video-Based Biometric Person Authentication (AVBPA '03), June 2003, Guildford, UK, Lecture Notes in Computer Science 2688: 62-69.View ArticleGoogle Scholar
  7. Gavrila DM, Davis LS: 3-D model-based tracking of humans in action: a multi-view approach. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '96), June 1996, San Francisco, Calif, USA 73-80.View ArticleGoogle Scholar
  8. Bowyer KW, Chang K, Flynn P: A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition. Computer Vision and Image Understanding 2006,101(1):1-15. 10.1016/j.cviu.2005.05.005View ArticleGoogle Scholar
  9. Yamada A, Cieplinski L: MPEG-7 Visual part of eXperimentation Model Version 17.1. 2003.Google Scholar
  10. Kamei T, Yamada A, Kim H, Hwang W, Kim T-K, Kee SC: CE report on Advanced Face Recognition Descriptor. 2002.Google Scholar
  11. Lee W-S, Sohn K-A: Face recognition using computer-generated database. In Proceedings of Computer Graphics International (CGI '04), June 2004, Crete, Greece. IEEE Computer Society Press; 561-568.Google Scholar
  12. Lee W-S, Sohn K-A: Database construction & recognition for multi-view face. In Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition (FGR '04), May 2004, Seoul, Korea. IEEE Computer Society Press; 350-355.Google Scholar
  13. Graham DB, Allinson NM: Characterizing virtual eigensignatures for general purpose face recognition. In Face Recognition: From Theory to Applications. Edited by: Wechsler H, Phillips PJ, Bruce V, Fogelman-Soulie F, Huang TS. Springer, Berlin, Germany; 1998:446-456.View ArticleGoogle Scholar
  14. Park G, Baek Y, Lee H-K: A ranking algorithm using dynamic clustering for content-based image retrieval. Proceedings of the International Conference Image and Video Retrieval (CIVR '02), July 2002, London, UK, Lecture Notes in Computer Science 2383: 328-337.View ArticleGoogle Scholar


© W.-S. Lee and K.-A. Sohn. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.