MutualBoost learning for selecting Gabor features for face recognition

Linlin Shen, Li Bai

Research output: Journal PublicationArticlepeer-review

86 Citations (Scopus)

Abstract

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 dimension, memory and computation costs are reduced significantly - only 4 s are needed to recognize 200 face images.

Original languageEnglish
Pages (from-to)1758-1767
Number of pages10
JournalPattern Recognition Letters
Volume27
Issue number15
DOIs
Publication statusPublished - Nov 2006
Externally publishedYes

Keywords

  • AdaBoost algorithm
  • Gabor filters
  • Generalized discriminant analysis

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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