A.N.N. Based Approach to Mass Biometry Taking Advantage from Modularity

  • Authors:

  • Kurosh Madani;Abdennasser Chebira;Véronique Amarger

  • Affiliations:

  • Images, Signals and Intelligence Systems Laboratory (LISSI / EA 3956), PARIS-EST / PARIS 12, Val de Marne University, Senart-FB Institute of Technology, Lieusaint, France F-77127;Images, Signals and Intelligence Systems Laboratory (LISSI / EA 3956), PARIS-EST / PARIS 12, Val de Marne University, Senart-FB Institute of Technology, Lieusaint, France F-77127;Images, Signals and Intelligence Systems Laboratory (LISSI / EA 3956), PARIS-EST / PARIS 12, Val de Marne University, Senart-FB Institute of Technology, Lieusaint, France F-77127

  • Venue:
  • IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
  • Year:
  • 2009

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Abstract

Over the recent years, new public security tendency to fit up public areas with biometric devices has emerged new requirements in biometric recognition dealing with what we call here "mass biometry". If the goal in "individual biometry" is to authenticate and/or identify an individual within a set of favored folks, the aim in "mass biometry" is to classify a suspect individual or behavior within a flow of mass customary information. In this case, the ability of handling relatively poor information and the skill of high speed processing become chief requirements. These antagonistic requests make the "mass biometry" and related applications among the most challenging frames. In this paper we present an ANN based system in a "mass biometry" context using facial biometric features. The proposed system takes advantage from kernel functions ANN model and IBM ZISC based hardware. Experimental results validating our system are presented and discussed.