SpringerOpen Newsletter

Receive periodic news and updates relating to SpringerOpen.

This article is part of the series Facial Image Processing.

Open Access Open Badges Research Article

View Influence Analysis and Optimization for Multiview Face Recognition

Won-Sook Lee1* and Kyung-Ah Sohn2

Author Affiliations

1 School of Information Technology and Engineering, University of Ottawa, Ottawa K1N6N5, Canada

2 Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213-3891, USA

For all author emails, please log on.

EURASIP Journal on Image and Video Processing 2007, 2007:025409  doi:10.1155/2007/25409

The electronic version of this article is the complete one and can be found online at: http://jivp.eurasipjournals.com/content/2007/1/025409

Received:1 May 2006
Revisions received:20 December 2006
Accepted:24 June 2007
Published:23 August 2007

© 2007 Lee and Sohn

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.

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.


  1. A Samal, PA Iyengar, Automatic recognition and analysis of human faces and facial expressions: a survey. Pattern Recognition 25(1), 65–77 (1992). Publisher Full Text OpenURL

  2. SZ Li, L Zhu, ZQ Zhang, A Blake, HJ Zhang, H Shum, 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

  3. Y Li, S Gong, H Liddell, 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

  4. G Shakhnarovich, L Lee, T Darrell, 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

  5. V Blanz, T Vetter, Face recognition based on fitting a 3D morphable model. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(9), 1063–1074 (2003). Publisher Full Text OpenURL

  6. AM Bronstein, MM Bronstein, R Kimmel, 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

  7. DM Gavrila, LS Davis, 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

  8. KW Bowyer, K Chang, P Flynn, A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition. Computer Vision and Image Understanding 101(1), 1–15 (2006). Publisher Full Text OpenURL

  9. A Yamada, L Cieplinski, MPEG-7 Visual part of eXperimentation Model Version 17.1 ISO/IEC JTC1/SC29/WG11 M9502, Pattaya, Thailand

  10. T Kamei, A Yamada, H Kim, W Hwang, T-K Kim, SC Kee, CE report on Advanced Face Recognition Descriptor ISO/IEC JTC1/SC29/WG11 M9178, Awaji, Japan

  11. W-S Lee, K-A Sohn, Face recognition using computer-generated database. Proceedings of Computer Graphics International (CGI '04), June 2004, Crete, Greece (IEEE Computer Society Press), pp. 561–568

  12. W-S Lee, K-A Sohn, Database construction & recognition for multi-view face. Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition (FGR '04), May 2004, Seoul, Korea (IEEE Computer Society Press), pp. 350–355

  13. DB Graham, NM Allinson, Characterizing virtual eigensignatures for general purpose face recognition. in Face Recognition: From Theory to Applications, ed. by Wechsler H, Phillips PJ, Bruce V, Fogelman-Soulie F, Huang TS (Springer, Berlin, Germany, 1998), pp. 446–456

  14. G Park, Y Baek, H-K Lee, 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