Abstract

This study investigates various feature selection techniques for face recognition. Biometric based authentication system protects access to resources and has gained importance, because of their reliable, invariant and discriminating features. An automated biometric system is based on physiological or behavioral human characteristics for protected access. Biometric trait such as palmprint, iris, hand, voice, face fingerprint, or signature is used to authenticate a person's claim. Of the biometrics, face recognition is gaining popularity due to its simple method of capturing the image using cameras. However the number of features generated is high leading to higher computation time. Using feature selection technique it is shown that recognition rate improves.

Keywords:

Biometrics, Correlation-based Feature Selection (CFS), face recognition, k Nearest Neighbor (kNN), Mutual Information (MI) , Naive Bayes (NB) , ORL face database,

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Competing interests

The authors have no competing interests.

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