Reduction of Feature Vectors Using Rough Set Theory for Human Face Recognition

Details Reduction of Feature Vectors Using Rough Set Theory for Human Face Recognition Reduction of Feature Vectors Using Rough Set Theory for Human Face Recognition

2010-05-21

arxiv.org/abs/1005.4044v1

ICCS 2005

Computer Science

Computer Vision and Pattern Recognition

Scientific paper

In this paper we describe a procedure to reduce the size of the input feature vector. A complex pattern recognition problem like face recognition involves huge dimension of input feature vector. To reduce that dimension here we have used eigenspace projection (also called as Principal Component Analysis), which is basically transformation of space. To reduce further we have applied feature selection method to select indispensable features, which will remain in the final feature vectors. Features those are not selected are removed from the final feature vector considering them as redundant or superfluous. For selection of features we have used the concept of reduct and core from rough set theory. This method has shown very good performance. It is worth to mention that in some cases the recognition rate increases with the decrease in the feature vector dimension.

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