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IPM
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YEARS OLD

“School of Cognitive Sciences”

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Paper   IPM / Cognitive Sciences / 11352
School of Cognitive Sciences
  Title:   Using biologically inspired visual features and mixture of experts for face/nonface recognition
  Author(s): 
1.  Zeinab Farhoudi
2.  Reza Ebrahimpour
  Status:   Published
  Journal: Lecture Notes in Computer Science
  Vol.:  5864
  Year:  2009
  Pages:   439-448
  Supported by:  IPM
  Abstract:
This paper introduces a novel, effective applicability of features inspired by visual ventral stream and biologically-motivated classification model, mixture of experts network for face/nonface recognition task. It describes a feature extracting system that derives from a feedforward model of visual cortex and builds a set of pose, facial expression, illumination and view invariant C1 features from all images in the dataset. Also, mixture of MLP experts network is a classifier which demonstrates high generalization capabilities in many different tasks. In accordance to these biological evidences, we propose face/nonface recognition model which combine these two techniques for the robust face/nonface problem. Experimental results using the combination C1 features and mixture of MLP experts network classifier, obtains higher recognition rate than related works in face/nonface identification. In addition, experimental results demonstrate this method is illumination and view-invariant.

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