Improved Statistical Techniques for Multi-part Face Detection and Recognition

Authors:
Christian Micheloni;Enver Sangineto;Luigi Cinque;Gian Luca Foresti
Affiliations:
Univeristy of Udine, Udine, 33100;University of Rome "Sapienza", Roma, 00198;University of Rome "Sapienza", Roma, 00198;Univeristy of Udine, Udine, 33100
Venue:
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Year:
2009

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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.