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2006

MutualBoost learning for selecting Gabor features for face recognition

9 years 9 months ago
MutualBoost learning for selecting Gabor features for face recognition
This paper describes an improved boosting algorithm, the MutualBoost algorithm, and its application in developing a fast and robust Gabor feature based face recognition system. The algorithm uses mutual information to eliminate redundancy among Gabor features selected using the AdaBoost algorithm. Selected Gabor features are then subjected to Generalized Discriminant Analysis (GDA) for class separability enhancement before being used for face recognition. Compared with one of the top performers in the 2004 face verification competition, our method demonstrates clear advantages in classification accuracy, memory and computation. The method has been tested on the whole FERET database using the FERET evaluation protocol. Significant improvement in performance is observed. For example, existing Gabor based methods use a huge number of Gabor features, our method needs only hundreds of Gabor features to achieve very high classification accuracy. Due to substantially reduced feature dimensio...
LinLin Shen, Li Bai
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2006
Where PRL
Authors LinLin Shen, Li Bai
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