DEVELOPMENT OF A DISCRETE-FIREFLY ALGORITHM BASED FEATURE SELECTION SCHEME FOR IMPROVED FACE RECOGNITION
DEVELOPMENT OF A DISCRETE-FIREFLY ALGORITHM BASED FEATURE SELECTION SCHEME FOR IMPROVED FACE RECOGNITION
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Date
2017-12
Authors
DANRAKA, Shittu Shehu
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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.
Description
A DISSERTATION SUBMITTED TO THE SCHOOL OF POSTGRADUATE STUDIES, AHMADU BELLO UNIVERSITY, ZARIA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF MASTER OF SCIENCE (M.Sc) DEGREE IN COMPUTER ENGINEERING
DEPARTMENT OF COMPUTER ENGINEERING, FACULTY OF ENGINEERING,
AHMADU BELLO UNIVERSITY, ZARIA, NIGERIA
Keywords
DEVELOPMENT,, DISCRETE-FIREFLY ALGORITHM,, RECOGNITION,