Face Recognition by Cortical Multi-scale Line and Edge Representations

  • João Rodrigues
  • J. M. Hans du Buf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4142)


Empirical studies concerning face recognition suggest that faces may be stored in memory by a few canonical representations. Models of visual perception are based on image representations in cortical area V1 and beyond, which contain many cell layers for feature extraction. Simple, complex and end-stopped cells provide input for line, edge and keypoint detection. Detected events provide a rich, multi-scale object representation, and this representation can be stored in memory in order to identify objects. In this paper, the above context is applied to face recognition. The multi-scale line/edge representation is explored in conjunction with keypoint-based saliency maps for Focus-of-Attention. Recognition rates of up to 96% were achieved by combining frontal and 3/4 views, and recognition was quite robust against partial occlusions.


Face Recognition Input Image Complex Cell Coarse Scale Simple Cell 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • João Rodrigues
    • 1
  • J. M. Hans du Buf
    • 2
  1. 1.Escola Superior de Tecnologia
  2. 2.Vision LaboratoryUniversity of AlgarveFaroPortugal

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