DEVELOPMENT OF A DISCRETE-FIREFLY ALGORITHM BASED FEATURE SELECTION SCHEME FOR IMPROVED FACE RECOGNITION
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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 was found to be 95.53% and R.T was 49.71 seconds. For the Yale database, the DFA had a R.A of 89.30% and a R.T of 40.33 seconds, for FA, the R.A was 85.33% and R.T was 43.65 seconds. On applying DFA on local images the R.A and R.T was 72.02% and 25.89 seconds respectively. The improvements in terms of R.A and R.T of this system when comparing DFA with FA on ORL database were 2.27% and 14.97%, while the improvements on Yale database were 4.45% and 7.61% respectively. Also, when compared with PCA, it gave an improvement of 25.48% in R.A and 23.82% in R.T, while for LDA it gave an improvement of 38.84% in R.A and 27.81% in R.T for ORL database. Also for the Yale database, when compared with PCA, it gave an improvement of 23.11% in R.A and 16.21% in R.T, and for LDA, it gave an improvement of 26.61% in R.A and 20.01% in R.T respectively
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