Using biologically inspired features for face processing

Ethan Meyers*, Lior Wolf

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

129 Scopus citations


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.

Original languageEnglish
Pages (from-to)93-104
Number of pages12
JournalInternational Journal of Computer Vision
Issue number1
StatePublished - Jan 2008


FundersFunder number
National Science FoundationEIA-0218506
National Institutes of Health1 P20 MH66239-01A1
Defense Advanced Research Projects AgencyFA8650-06-7632, HR0011-04-1-0037
National Defense Science and Engineering Graduate


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


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