International Journal of Computer Vision

, Volume 76, Issue 1, pp 93–104 | Cite as

Using Biologically Inspired Features for Face Processing

Short paper


In this paper, we show that a new set of visual features, derived from a feed-forward model of the primate visual object recognition pathway proposed by Riesenhuber and Poggio (R&P Model) (Nature Neurosci. 2(11):1019–1025, 1999) is capable of matching the performance of some of the best current representations for face identification and facial expression recognition. Previous work has shown that the Riesenhuber and Poggio Model features can achieve a high level of performance on object recognition tasks (Serre, T., et al. in IEEE Comput. Vis. Pattern Recognit. 2:994–1000, 2005). Here we modify the R&P model in order to create a new set of features useful for face identification and expression recognition. Results from tests on the FERET, ORL and AR datasets show that these features are capable of matching and sometimes outperforming other top visual features such as local binary patterns (Ahonen, T., et al. in 8th European Conference on Computer Vision, pp. 469–481, 2004) and histogram of gradient features (Dalal, N., Triggs, B. in International Conference on Computer Vision & Pattern Recognition, pp. 886–893, 2005). Having a model based on shared lower level features, and face and object recognition specific higher level features, is consistent with findings from electrophysiology and functional magnetic resonance imaging experiments. Thus, our model begins to address the complete recognition problem in a biologically plausible way.


Biologically motivated computer vision Face identification Face recognition Learning distance measures Kernel methods 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Ahonen, T., Hadid, A., & Pietikainen, M. (2004). Face recognition with local binary patterns. In 8th European conference on computer vision (pp. 469–481). Google Scholar
  2. Bar-Hillel, A., Hertz, T., Shental, N., & Weinshall, D. (2005). Learning a mahalanobis metric from equivalence constraints. Journal of Machine Learning Research, 6, 937–965. MathSciNetGoogle Scholar
  3. Bieschi, S., & Wolf, L. (2005). A unified system for object detection, texture recognition and cotext analysis based on the standard model feature set. In Proceedings of the British machine vision conference. Google Scholar
  4. Bolme, D. S., Beveridge, J. R., Teixeira, M., & Draper, B. A. (2003). The CSU face identification evaluation system: its purpose, features and structure. In Conference on vision systems (pp. 304–311). Google Scholar
  5. Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In Conference on computer vision and pattern recognition (pp. 886–893). Google Scholar
  6. Etemad, K., & Chellappa, R. (1997). Discriminant analysis for recognition of human face images. Journal of Optical Society of America, 14, 1724–1733. Google Scholar
  7. Fei-Fei, L., Fergus, R., & Perona, P. (2004). Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In CVPR, workshop on generative-model based vision. Google Scholar
  8. Fergus, R., Perona, P., & Zisserman, A. (2003). Object class recognition by unsupervised scale-invariant learning. In Conference on computer vision and pattern recognition (Vol. 2, pp. 264–271). Google Scholar
  9. Fukushima, K. (1980). Neocognitron: a self organizing neural network model for a mechanism for pattern recognition unaffected by shift in position. Biological Cybernetics, 36(4), 193–202. CrossRefMATHGoogle Scholar
  10. Goldberger, J., Roweis, S., Hinton, G., & Salakhutdinov, R. (2004). Neighbourhood component analysis. Neural Information Processing Systems, 17, 513–520. Google Scholar
  11. Hastie, T., & Tibshirani, R. (1996). Discriminant adaptive nearest neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(6), 607–616. CrossRefGoogle Scholar
  12. Jones, M., & Viola, P. (2003). Face recognition using boosted local features. In Proceedings of international conference on computer vision. Google Scholar
  13. Kanwisher, N., McDermott, J., & Chun, M. M. (1997). The fusiform face area: a module in human extrastriate cortex specialized for face perception. Journal of Neuroscience, 17(11), 4302–4311. Google Scholar
  14. Lowe, D. G. (2003). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110. CrossRefGoogle Scholar
  15. Martinez, A. M., & Benavente, R. (1998). The AR face database. CVC Technical Report #24. Google Scholar
  16. Moghaddam, B., Nastar, C., & Pentland, A. (1996). A Bayesian similarity measure for direct image matching. In Conference on computer vision and pattern recognition (p. 638). Google Scholar
  17. Mutch, J., & Lowe, D. (2006). Multiclass object recognition using sparse, localized features. In Conference on computer vision and pattern recognition (pp. 11–18). Google Scholar
  18. Ojala, T., Pietikainen, M., & Harwood, D. (1996). A comparative study of texture measures with classification based on feature distributions. Pattern Recognition, 29, 51–59. CrossRefGoogle Scholar
  19. Olshausen, B. A., & Field, D. J. (1997). Sparse coding with an overcomplete basis set: a strategy employed by V1? Vision Research, 37, 3311–3325. CrossRefGoogle Scholar
  20. Phillips, P. J., Moon, H., Rizvi, S. A., & Rauss, P. J. (2002). The FERET evaluation methodology for face recognition algorithms. Pattern Analysis and Machine Intelligence, 22(10), 1090–1104. CrossRefGoogle Scholar
  21. Pontil, M., & Verri, A. (1998). Support vector machines for 3D object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(6), 637–646. CrossRefGoogle Scholar
  22. Riesenhuber, M., & Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nature Neuroscience, 2(11), 1019–1025. CrossRefGoogle Scholar
  23. Samaria, F., & Harter, A. (1994). Parameterisation of a stochastic model for human face identification. In 2nd IEEE workshop on applications of computer vision. Google Scholar
  24. Schölkopf, B., & Smola, A. J. (2002). Learning with kernels. Cambridge: MIT. Google Scholar
  25. Serre, T., Wolf, L., & Poggio, T. (2005). Object recognition with features inspired by visual cortex. In Conference on computer vision and pattern recognition (Vol. 2, pp. 994–1000). Google Scholar
  26. Shan, C., Gong, S., & McOwan, P. (2005). Conditional mutualinformation based boosting for facial expression recognition. In Proceedings of the British machine vision conference. Google Scholar
  27. Turk, M., & Pentlanbd, A. (1991). Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3, 71–86. CrossRefGoogle Scholar
  28. Ungerleider, L. G., & Mishkin, M. (1982) Two cortical visual systems. In: Analysis of visual behavior, (pp. 549–586). Cambridge: MIT. Google Scholar
  29. Wang, H., Li, S., & Wang, Y. (2004). Face recognition under varying lighting conditions using self quotient image. In IEEE international conference on automatic face and gesture recognition (pp. 819–824). Google Scholar
  30. Weber, M., Welling, M., & Perona, P. (2000). Unsupervised learning of models for recognition. In European conference on computer vision (pp. 19–32). Google Scholar
  31. Zigmond, M. J. (1999). Fundamental neuroscience (1st ed.). New York: Academic Press. Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.The Center for Biological and Computational LearningMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.School of Computer ScienceTel-Aviv UniversityTel AvivIsrael

Personalised recommendations