Face recognition using transform domain feature extraction and PSO-based feature selection |
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Affiliation: | 1. MS Ramaiah Institute of Technology, Bangalore, Karnataka 560054, India;2. SJB Institute of Technology, Bangalore, Karnataka 560060, India;1. Department of Electronics Technology, Industrial Technical Engineering School of Bilbao, University of the Basque Country (UPV/EHU), Paseo Rafael Moreno 3, 48013 Bilbao, Basque Country, Spain;2. Department of Electricity and Electronics, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Leioa, Barrio Sarriena s/n, 48940 Basque Country, Spain;1. Department of Applied Mathematics and Computer Science, Ghent University, Belgium;2. Department of Computer Science, University of Camagüey, Cuba;3. Department of Computer Science and AI, University of Granada, Spain;4. Faculty of Computing and Information Technology, North Jeddah, King Abdulaziz University, Jeddah, Saudi Arabia;1. Department of Computer Science and Engineering, University of Calcutta, Kolkata 700009, India;2. National Institute of Technology – Puducherry, Karaikal 609605, India;1. UAEMEX (Autonomous University of Mexico State), Texcoco 56259, Mexico;2. Division of Research and Postgraduate Studies, Instituto Tecnológico de Orizaba, Veracruz 94320, Mexico;3. UAEMEX (Autonomous University of Mexico State), Zumpango 55600, Mexico |
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Abstract: | This paper presents two new techniques, viz., DWT Dual-subband Frequency-domain Feature Extraction (DDFFE) and Threshold-Based Binary Particle Swarm Optimization (ThBPSO) feature selection, to improve the performance of a face recognition system. DDFFE uses a unique combination of DWT, DFT, and DCT, and is used for efficient extraction of pose, translation and illumination invariant features. The DWT stage selectively utilizes the approximation coefficients along with the horizontal detail coefficients of the 2-dimensional DWT of a face image, whilst retaining the spatial correlation of pixels. The translation variance problem of the DWT is compensated in the following DFT stage, which also exploits the frequency characteristics of the image. Then, all the low frequency components present at the center of the DFT spectrum are extracted by drawing a quadruple ellipse mask around the spectrum center. Finally, DCT is used to lay the ground for BPSO based feature selection. The second proposed technique, ThBPSO, is a novel feature selection algorithm, based on the recurrence of selected features, and is used to search the feature space to obtain a feature subset for recognition. Experimental results obtained by applying the proposed algorithm on seven benchmark databases, namely, Cambridge ORL, UMIST, Extended Yale B, CMUPIE, Color FERET, FEI, and HP, show that the proposed system outperforms other FR systems. A significant increase in the recognition rate and a substantial reduction in the number of features required for recognition are observed. The experimental results indicate that the minimum feature reduction obtained is 98.2% for all seven databases. |
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Keywords: | Face recognition Feature selection Binary Particle Swarm Optimization Discrete Wavelet Transform Discrete Fourier Transform Discrete Cosine Transform |
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