Improved Statistical Techniques for Multi-part Face Detection and Recognition

  • Christian Micheloni
  • Enver Sangineto
  • Luigi Cinque
  • Gian Luca Foresti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5575)

Abstract

In this paper we propose an integrated system for face detection and face recognition based on improved versions of state-of-the-art statistical learning techniques such as Boosting and LDA. Both the detection and the recognition processes are performed on facial features (e.g., the eyes, the nose, the mouth, etc) in order to improve the recognition accuracy and to exploit their statistical independence in the training phase. Experimental results on real images show the superiority of our proposed techniques with respect to the existing ones in both the detection and the recognition phase.

Keywords

Face Recognition Face Detection Face Pattern Lower Dimensional Feature Space Facial Feature Detection 
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 2009

Authors and Affiliations

  • Christian Micheloni
    • 1
  • Enver Sangineto
    • 2
  • Luigi Cinque
    • 2
  • Gian Luca Foresti
    • 1
  1. 1.Univeristy of UdineUdineItaly
  2. 2.University of Rome “Sapienza”RomaItaly

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