Open Access

Multiclient Identification System Using Adaptive Probabilistic Model

EURASIP Journal on Advances in Signal Processing20102010:983581

Received: 1 December 2009

Accepted: 14 April 2010

Published: 23 May 2010


This paper aims at integrating detection and identification of human faces in a more practical and real-time face recognition system. The proposed face detection system is based on the cascade Adaboost method to improve the precision and robustness toward unstable surrounding lightings. Our Adaboost method innovates to adjust the environmental lighting conditions by histogram lighting normalization and to accurately locate the face regions by a region-based-clustering process as well. We also address on the problem of multi-scale faces in this paper by using 12 different scales of searching windows and 5 different orientations for each client in pursuit of the multi-view independent face identification. There are majorly two methodological parts in our face identification system, including PCA (principal component analysis) facial feature extraction and adaptive probabilistic model (APM). The structure of our implemented APM with a weighted combination of simple probabilistic functions constructs the likelihood functions by the probabilistic constraint in the similarity measures. In addition, our proposed method can online add a new client and update the information of registered clients due to the constructed APM. The experimental results eventually show the superior performance of our proposed system for both offline and real-time online testing.


Face RecognitionFace DetectionProbabilistic ConstraintFunction ConstructFace Recognition System

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Authors’ Affiliations

Department of Electrical and Control Engineering, National Chiao Tung University, Hsinchu, Taiwan
Department of Computer and Communication Engineering, China University of Technology, Hsinchu, Taiwan


© Chin-Teng Lin et al. 2010

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.