On the Role of Object-Specific Features for Real World Object Recognition in Biological Vision

  • Thomas Serre
  • Maximilian Riesenhuber
  • Jennifer Louie
  • Tomaso Poggio
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2525)


Models of object recognition in cortex have so far been mostly applied to tasks involving the recognition of isolated objects presented on blank backgrounds. However, ultimately models of the visual system have to prove themselves in real world object recognition tasks. Here we took a first step in this direction: We investigated the performance of the HMAX model of object recognition in cortex recently presented by Riesenhuber & Poggio [1],[2] on the task of face detection using natural images. We found that the standard version of hmax performs rather poorly on this task, due to the low specificity of the hardwired feature set of C2 units in the model (corresponding to neurons in intermediate visual area V4) that do not show any particular tuning for faces vs. background. We show how visual features of intermediate complexity can be learned in HMAX using a simple learning rule. Using this rule, hmax outperforms a classical machine vision face detection system presented in the literature. This suggests an important role for the set of features in intermediate visual areas in object recognition.


Object Recognition Face Image Face Detection Background Pattern Blank Background 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    M. Riesenhuber and T. Poggio. Hierarchical models of object recognition in cortex. Nat. Neurosci., 2(11):1019–25, 1999.CrossRefGoogle Scholar
  2. 2.
    M. Riesenhuber and T. Poggio. Models of object recognition. Nature Neuroscience, 3 supp.:1199–1204, 2000.Google Scholar
  3. 3.
    B. Heisele, T. Serre, M. Pontil, and T. Poggio. Component-based face detection. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, volume 1, pages 657–62, Hawaii, 2001.Google Scholar
  4. 4.
    K.-K. Sung. Learning and Example Selection for Object and Pattern Recognition. PhD thesis, MIT, Artificial Intelligence Laboratory and Center for Biological and Computational Learning, Cambridge, MA, 1996.Google Scholar
  5. 5.
    D. Hubel and T. Wiesel. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Phys., 160:106–54, 1962.Google Scholar
  6. 6.
    T. J. Gawne and J. M. Martin. Response of primate visual cortical V4 neurons to simultaneously presented stimuli. To appear in J. Neurophysiol., 2002.Google Scholar
  7. 7.
    D. J. Freedman, M. Riesenhuber, T. Poggio, and E. K. Miller. Categorical representation of visual stimuli in the primate prefrontal cortex. Science, 291:312–16, 2001.CrossRefGoogle Scholar
  8. 8.
    V. Vapnik. The nature of statistical learning. Springer Verlag, 1995.Google Scholar
  9. 9.
    T. Vetter. Synthesis of novel views from a single face. International Journal of Computer Vision, 28(2):103–116, 1998.MathSciNetCrossRefGoogle Scholar
  10. 10.
    S. Ullman, M. Vidal-Naquet, and E. Sali. Visual features of intermediate complexity and their use in classification. Nat. Neurosci., 5(7):682–87, 2002.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Thomas Serre
    • 1
  • Maximilian Riesenhuber
    • 1
  • Jennifer Louie
    • 1
  • Tomaso Poggio
    • 1
  1. 1.Artificial Intelligence Lab, and Department of Brain and Cognitive Sciences, Massachusetts Institute of TechnologyCenter for Biological and Computational Learning, Mc Govern Institute for Brain ResearchCambridgeUSA

Personalised recommendations