Adaptively weighted orthogonal gradient binary pattern for single sample face recognition under varying illumination

Abstract

To overcome the limitation of traditional illumination invariant methods for single sample face recognition, a modified version of gradientface named adaptively weighted orthogonal gradient binary pattern (AWOGBP), which is proved robust to illumination variation, is proposed in this study. First, the Tetrolet transform is performed on the images to obtain low frequency and high frequency components and the retina model processing is applied to low frequency component to make the image more robust to illumination, in the meantime, the authors multiply each element in high frequency components with a scale factor to accentuate details. Then the proposed AWOGBP is used to get the feature vectors of each direction and all the feature vectors are concatenated into the general feature vector for face recognition with the weights of the sub-graph based on their information entropy which is defined as the contribution to describe the whole face images. Finally the principle component analysis method is used to reduce dimensions and the nearest neighbour classifier is used for face image classification and recognition. Experimental results on CMU PIE and Extended Yale B face databases indicate that the proposed method is significantly better as compared with related state-of-the-art methods.

Neuro-Fuzzy Hybridization using Modified S Membership Function and Kernel Extreme Learning Machine for Robust Face Recognition under Varying Illuminations

“…Hybridization of two techniques namely DCT and discrete wavelet transform (DWT) were utilized by Vishwakarma and Goel in 2019 and illumination normalization was performed over various standard illumination varying face databases with the help of principal component analysis (PCA) with k-NNC classifier and back-propagation (BP) classifier [14]. There were some more techniques which have contributed in illumination normalization and that are: 9PL [15], Fractal analysis + Log function [16], Tetrolet transform [17], Edge orientation [18], etc.…”

A Study on Image Retrieval Based on Tetrolet Transform

“…Further study can also be well extended to otherimage processing areas such as face recognition, edge detection and video editing etc. Yang Hui-xian, [10] in their paper, to overcome the limitation of traditional illumination invariant methods for single sample face recognition, a modified version of gradientface named adaptively weighted orthogonal gradient binary pattern (AWOGBP)is proposed, which is proved to be robust to illumination variation. Tetrolet decomposition is used to get different band information of face images.…”