Face Recognition using Truncated Transform Domain Feature Extraction

Rangan Kodandaram ¹, Shashank Mallikarjun ¹ Manikantan Krishnamuthan ¹and, Ramachandran Sivan²

¹Ramaiah Institute of Technology, Bangalore, India

²S.J.B Institute of Technology, Bangalore, India

 Abstract: Face Recognition (FR) under varying pose is challenging and exacting pose invariant features is an effective approach to solve this problem. In this paper, we propose a novel Truncated Transform Domain Feature Extractor (TTDFE) to improve the performance of the FR system. TTDFE involves a unique combination of   Symlet - 4 DWT, 2D - DCT, followed by a novel truncation process. The truncation process extracts higher amplitude coefficients from the Discrete Cosine Transform (DCT) matrix. An optimal truncation point is estimated, which is inspired by a relationship developed between the image dimensions and the positions of DCT amplitude peaks. TTDFE is used for efficient feature extraction and a Binary Particle Swarm Optimization - based feature selection algorithm is used to search the feature space for the optimal feature subset. Experimental results, obtained by applying the proposed algorithm on 5 benchmark  face databases with large pose variations, namely FERET, UMIST, FEI, PHPD and IFD, 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 selected, are observed.

 Keywords: FR, feature extraction, discrete wavelet transform, discrete cosine transform, feature selection, binary particle swarm optimization.

 Received January 28, 2013; accept March 2, 2014

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