DEVELOPMENT OF A DISCRETE-FIREFLY ALGORITHM BASED FEATURE SELECTION SCHEME FOR IMPROVED FACE RECOGNITION

📄 Project Abstract

This research presents the development of a Discrete Firefly Algorithm (DFA) based feature selection scheme for improved face recognition. Discrete Cosine Transform (DCT) and Haar wavelet based Discrete Wavelet Transform (DWT) were used for feature extraction, and Nearest Neighbour Classifier (NNC) was used as classifier. Extracted features are mostly discrete in nature and most of the optimization techniques used in feature selection are continuous so DFA is employed for feature selection. The developed DFA based feature selection scheme was tested on Olivetti Research Labs (ORL) and Yale databases, and was compared with Firefly Algorithm (FA), Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) respectively. The simulation was carried out in MATLAB R2013b simulation environment, and the result obtained from ORL database for DFA showed that the recognition accuracy (R.A) was found to be 97.75 % and recognition time (R.T) was 42.27 seconds while for FA, the R.A wa...