Face Recognition using Entropy based Face Segregation as a Pre-processing Technique and Conservative BPSO based Feature Selection

Abstract

The statistical appearance of the face can vary due to various factors such as pose, occlusion, expression and background which makes it a challenging task to have an efficient Face Recognition (FR) system. This paper proposes 4 novel techniques viz., Entropy based Face Segregation (EFS) as pre-processing technique, Double Wavelet Noise Removal (DWNR) as pre-processing technique, 1D Stationary Wavelet Transform (SWT) as Feature Extractor and Conservative Binary Particle Optimization (CBPSO) as Feature Selector to enhance the performance of the system. EFS is used to segregate the facial region, thus removing the cluttered background. DWNR has unique combination of 2D Discrete Wavelet Transform (DWT), Wiener Filter and 2D SWT for image denoising and contrast enhancement. The pre-processed image is then fed to unique combination of 1D DWT, 1D SWT and 1D Discrete Cosine Transform (DCT) to extract essential features. CBPSO is used to select very optimum feature subset and significantly reduce the computation time. The proposed algorithm is experimented on four benchmark databases viz., Color FERET, CMU PIE, Pointing Head Pose and Georgia Tech.