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

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.

• Type: Journal
• Year: 2010
• Where: CORR
• Authors: Debotosh Bhattacharjee, Dipak Kumar Basu, Mita Nasipuri, Mohantapash Kundu
• Keywords: CORR 2010, Education, Feature Vector, Feature Vector Dimension, Final Feature Vector