Low Frequency DD-DWT based illumination compensated and color intensified features for Face Recognition

Abstract

The efficacy of face recognition systems is significantly affected by uneven light deviations, incident over images from different directions. This work presents a light invariable color face recognition method that efficiently normalizes illumination variances along with substantial improvement in color intensities. The proposed method includes following steps: Initially, segregation of facial images into primary spectral color components is performed to effectively balance contrast and evenly amplify color intensity levels. The varying illumination effects are due to low frequency component of image; therefore, Difference of Gaussian (DoG) high-pass filter based homomorphic filtering is further utilized in HSI (Hue, Saturation and Intensity) space. This conversion into HSI color space is mainly performed to ward off any colors distortion that may prevail in images due to direct utilization of further improvements on each color channel. Thus, this model aids in easily separating the chromaticity component from intensity part of an image. The normalized intensity component is concatenated with unaltered chromaticity components to effectively possess color facial information. Subsequently, extraction of double density discrete wavelet transform (DD-DWT) based coefficients of images is achieved for selection of substantial discriminative frequency components of face images. The large facial feature vector space is hereafter projected over eigen subspace to reduce dimensions effectively. In this work, k-nearest neighbor linear classifier has been utilized to classify selective features. The effectiveness of investigated method has been assessed on AR and CMU-PIE color face databases and attained results are thus contrasted with prior techniques which benefit in establishing its superiority.