Firefly Inspired Feature Selection for Face Recognition

Abstract

In this paper, an adaptive technique using Firefly Algorithm for feature selection in face recognition is proposed. The artificial fireflies are designed to represent the feature subset and they move in a hyper dimensional space to obtain the best features. The features are extracted using Discrete Cosine Transform (DCT) and Haar wavelets based Discrete Wavelet Transform (DWT). The algorithm is validated using benchmark face databases namely ORL and Yale. The proposed algorithm outperforms various existing techniques. The average recognition accuracy using five randomly selected training samples over four independent runs for the ORL is 94.375%. The accuracy using six training images for Yale face database is 99.16%. The effect of parameter 'gamma', specific to Firefly Algorithm on recognition accuracy is also investigated.