Efficient feature selection based on information gain criterion for face recognition

Feature selection based on information gain criterion is proposed for improvements in classification performance of face recognition tasks. A comparison of information gain with fisher criterion is presented for two different image database. Information gain criterion gives slight performance improvement when ICA based features are used for recognition of facial images with different illumination. For less number of selected features, information gain criterion gives superior performance compared to fisher criterion. However, the performance of information gain and fisher method for feature selection shows similar performance when the database has classes with different illusion, expressions, and occlusions.