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. 
Face recognition using transform domain feature extraction and PSO-based feature selection