DocumentCode :
501144
Title :
Ensemble Methods of Face Recognition Based on Bit-plane Decomposition
Author :
Li, Kai ; Wang, Lingxiao
Author_Institution :
Sch. of Math. & Comput., Hebei Univ., Baoding, China
Volume :
1
fYear :
2009
fDate :
6-7 June 2009
Firstpage :
194
Lastpage :
197
Abstract :
Face recognition has become one of the latest research subjects of pattern recognition and image processing. Although many face recognition techniques have been proposed and many achievements have been obtained, we canpsilat get high recognition rate due to the changes of face expression, location, direction and light. In this paper we study human face recognition based on ensemble techniques. In order to improve diversity of component classifiers, the idea of bit-plane decomposition is used and moving window classifier is used as a basic individual classifier. The quantized pattern representationspsila layers are used jointly to make a decision. And we mainly study several fused methods which include product, sum, majority vote, max, min and median rules. Experiments results with face images databases show that fusion of multiple classifiers has good classification performance. Moreover, we compare different multiple classifier schemes with other human face recognition methods.
Keywords :
face recognition; pattern classification; bit-plane decomposition; component classifier; ensemble technique; face expression; face image database; face recognition; image processing; moving window classifier; pattern recognition; quantized pattern representation; Control systems; Diversity reception; Face detection; Face recognition; Feature extraction; Humans; Image processing; Image recognition; Pattern recognition; Voting; bit-plane decomposition; ensemble method; face recognition; fusion; moving window classifier;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Natural Computing, 2009. CINC '09. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3645-3
Type :
conf
DOI :
10.1109/CINC.2009.216
Filename :
5231173
Link To Document :
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