Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain

Abstract

This paper presents a novel illumination normalization approach for face recognition under varying lighting conditions. In the proposed approach, a discrete cosine transform (DCT) is employed to compensate for illumination variations in the logarithm domain. Since illumination variations mainly lie in the low-frequency band, an appropriate number of DCT coefficients are truncated to minimize variations under different lighting conditions. Experimental results on the Yale B database and CMU PIE database show that the proposed approach improves the performance significantly for the face images with large illumination variations. Moreover, the advantage of our approach is that it does not require any modeling steps and can be easily implemented in a real-time face recognition system.

Illumination Insensitive Face Representation for Face Recognition Based on Modified Weberface

“…The first experiment was conducted to use all the images from subset 1 as the gallery images, and the other images from subset 2 to 5 as the probe images. We compared OWF, WF2, WF3, WF4 with the conventional Weberface (WF) [16] and several other state-of-the-art methods including HE [8], DCT [14], WA [22], SQI [13], ASR [15] and GRF [5]. Figure 6 shows 10 faces under various illumination conditions and the corresponding illumination insensitive face representations using different methods.…”

“…Self quotient image (SQI) [13] also uses a smoothed version as the estimation of L. The smoothing is realized by weighted Gaussian filters. In [14], W. Cheng et al proposed a method based on Discrete Cosine Transform (DCT), which deems L to be the first n low frequency components of the transformed image. The component R is eventually attained by a subtraction since the input image is firstly projected into the logarithmic domain.…”

Face Recognition System Using PCA and DCT in HMM

“…In Discrete Cosine Transform (DCT), a series of finitely several data points are expressed in terms of a sum of cosine functions oscillating at diverse frequencies [26,27]. It can help to extract the feature of face image by apply two dimensional Discrete Cosine Transform(DCT) because the coefficients of most upper region and most left region in DCT transform represent edge information [28,29].…”

Invariant Illumination Face Recognition Using Optimized PNN Topology through Neuron Orthogonalization and Genetic Algorithm

“…Chen et. al [4] then proposed an illumination normalization approach to remove the illumination variations while keeping the main facial features remain. This approach is accomplished by truncating the low-frequency discrete cosine transform (DCT) coefficients in its logarithm domain.…”