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Multiple scale neural architecture for face recognition

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Abstract

In this paper, we present a multiple scale neural architecture for face recognition. The architecture is composed of several stages: face detection, Difference of Gaussians, Gabor filter bank, Principal Component Analysis, and two-stage MLPs. The architecture was evaluated using two well-known face databases. A detailed study of all the parameters that influence the architecture performance was carried out. The architecture achieved a correct detection rate of 84% with face images changing in pose and gesture.

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Authors and Affiliations

  1. Higher School of Telecommunications Engineering, University of Valladolid, Valladolid, Spain

    D. González-Ortega, F. J. Díaz-Pernas, M. Antón-Rodríguez, M. Martínez-Zarzuela, I. de la Torre-Díez, D. Boto-Giralda & J. F. Díez-Higuera

Authors
  1. D. González-Ortega
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  2. F. J. Díaz-Pernas
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  3. M. Antón-Rodríguez
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  4. M. Martínez-Zarzuela
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  5. I. de la Torre-Díez
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  6. D. Boto-Giralda
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  7. J. F. Díez-Higuera
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Correspondence to D. González-Ortega.

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González-Ortega, D., Díaz-Pernas, F.J., Antón-Rodríguez, M. et al. Multiple scale neural architecture for face recognition. Pattern Recognit. Image Anal. 21, 387–391 (2011). https://doi.org/10.1134/S1054661811020362

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  • DOI: https://doi.org/10.1134/S1054661811020362

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