Illumination Normalization For Face Recognition Research Articles

Normalization of face illumination with photorealistic texture via deep image prior synthesis

Illumination normalization for face recognition is of great importance when the face image is taken under a harsh lighting condition and only one single image is available. In this paper, an illumination normalization method that can preserve face identity and make the processed image have photorealistic face texture is proposed. First, an illumination regression filter is proposed to remove a large number of illumination components of the input image. Then, the accelerated proximal gradient algorithm is used for low-rank decomposition of the filtered image, which further reduces the residual illumination and noise of the face image. However, after these two operations, the face image has a peeled-off appearance. To avoid the unrealistic appearance, a deep image prior synthesis process is developed to synthesize the photorealistic face texture. To accomplish this task, the filtered image, the low-rank decomposed image, the original image and any a uniformly illuminated frontal face image as reference are used as synthetic ingredients. These ingredients, as prior knowledge, synthesize a face image that has the texture features of the reference image and retains the identity of the input image. We try to bridge the appearance gap between the synthetic ingredients and the final synthetic image by a deep neural network. Instead of training the network on a large dataset, the final synthetic image is generated by iterating the parameters of the generator-structured network according to a certain proportion of the synthetic ingredients. Systematic evaluations conducted on public databases demonstrate that the proposed method is robust to illumination with better performance than state-of-the-art methods.

A novel non-linear modifier for adaptive illumination normalization for robust face recognition

In this paper, a novel approach is presented for adaptive illumination normalization for face recognition under varying illuminations due to change in angle of light projection. Illumination normalization is performed over some of the low frequency discrete Cosine transform (DCT) coefficients which are computed adaptively based upon the significance of alteration of these coefficients. These are, then, modified using a non-linear modifier. The significance of the proposed approach is that the approach is adaptive in normalizing the illumination from the face images as the number of low frequency DCT coefficients that need to be modified using non-linear modifier are selected on the basis of variations of illumination present in the image. This variation in the illumination is also used in developing the non-linear modifier. The proposed approach is important in such a way that the level of illumination variations decides the computations needed i.e. lesser in comparison to the other existing state-of-art approaches as large number of frequency coefficients are not altered. The proposed approach is tested over various face databases: YALE B, Extended YALE B, CMU PIE, AR and YALE face database. The experimental results clearly reveal that the performance of the proposed approach is significantly better than the existing approaches of illumination normalization for face recognition. With the proposed approach, 100% accuracy is attained on all the subsets of YALE B, CMU PIE and on Subset 3 of Extended YALE B database. Promising results are achieved over remaining face databases as well.

High-Fidelity Illumination Normalization for Face Recognition Based on Auto-Encoder

Nonuniform illumination is one of the main issues that hinder the accuracy of face recognition because it makes the intra-person variation more complicated. To minimize the intra-person differences caused by varying illuminations, this paper presents a normalization method based on Convolutional Auto-encoder (CAE). The CAE is employed to map the face images under various illumination conditions to a normalized one, generating preliminary results with blurry and insufficient facial details, which are tricky for recognition. To recover these details, a restoration method based on re-blurring strategy and frequency analysis is proposed to preserve the facial features lying in high-frequency components based on discrete cosine transform (DCT). Therefore, in our method, these components are extracted and re-introduced into the outputs of CAE to enhance the fidelity of outputs. Thus, the facial details are preserved to the largest degree and the following works such as recognition tasks are benefited. Experiments conducted on the AR, extended Yale B, and CAS-PEAL database demonstrate the effectiveness of our method.

Asymmetric Joint GANs for Normalizing Face Illumination From a Single Image

Illumination normalization for face recognition is very important when a face is captured under harsh lighting conditions. Instead of designing hand-crafted features, in this paper we formulate face illumination normalization as an image-to-image translation task. A great challenge of face normalization is that human facial structures are particularly sensitive to image structure distortion, which frequently occurs in traditional image-to-image translation tasks. Unfortunately, sometimes even slight facial structure distortions may prohibit human eyes and machine face recognition methods from identifying face identities. To address this issue, a novel GAN-based network architecture called the asymmetric joint generative adversarial network (AJGAN) is developed to normalize face images under arbitrary illumination conditions, without known face geometry and albedo information. In addition, an illumination normalization GAN $G_1$ and an asymmetric relighting GAN $G_2$ that maps a frontal-illuminated image to images with various lighting conditions are incorporated in AJGAN to maintain personalized facial structures. To avoid image blurring caused by the under-constrained relighting mapping, we introduce a scheme of one-hot lighting labels into $G_2$ and enforce label classification loss. Furthermore, the number of training images starting from a very limited number of labels is dynamically extended by the combination of different lighting labels. Qualitative and quantitative experiments on three databases validate that AJGAN significantly outperforms the state-of-the-art methods.

An efficient hybrid DWT-fuzzy filter in DCT domain based illumination normalization for face recognition

This paper presents an efficient hybrid DWT-DCT based illumination normalization technique for face recognition. In a face image, illumination usually changes slowly compared to the reflectance except some casting shadows and specularities on the face. Consequently, illumination variations mainly lie in the low frequency band of the face image. Therefore, in the present work, low frequency coefficients are processed to nullify the effect of illumination variations. Discrete wavelet transform (DWT) is used to decompose the image into frequency domain. It is a sub-band coding technique which decomposes image into four sub-bands: low-low (LL), low-high (LH), high-low (HL) and high-high (HH). As illumination is related to low frequency coefficients, normalization is mainly performed on LL sub-band rather than the whole face. The fuzzy filter is applied on the appropriate number of low frequency discrete Cosine transform (DCT) coefficients of LL sub-band to minimize the variations under different lighting conditions. Also, minor corrections are performed on the rest three sub-bands. After modification, the normalized LL sub-band and rest three sub-bands are combined to generate the normalized face image. The given approach achieves zero error rates on Yale B and CMU PIE face database. Also, good performance results have been achieved on Extended Yale B face database. These results clearly confirm the effectiveness of the given approach of illumination normalization.

Illumination Normalization for Face Recognition via Jointly Optimized Dictionary-Learning and Sparse Representation

In this paper, we present a novel patch-based dictionary learning (DL) framework for face illumination normalization (IN). The proposed method is based on sparse representation that is from the coupled dictionaries (DL-IN). The used dictionary pairs in this paper are jointly optimized from paired face images with normal and irregular illuminations. A Gaussian mixture model (GMM) clustering was further used to improve the capability of modeling data with more complex distributions. The proposed GMM adapts individual model based on a portion of the samples and lastly fuses all the images together. Experimental results show that the proposed framework by jointly optimizing DL and incorporating structural sparsity as a prior can improve the accuracy for face recognition.

Illumination Normalization for Face Recognition Using Energy Minimization Framework

Illumination Normalization for Face Recognition Using Energy Minimization Framework

Enhanced facial texture illumination normalization for face recognition.

An uncontrolled lighting condition is one of the most critical challenges for practical face recognition applications. An enhanced facial texture illumination normalization method is put forward to resolve this challenge. An adaptive relighting algorithm is developed to improve the brightness uniformity of face images. Facial texture is extracted by using an illumination estimation difference algorithm. An anisotropic histogram-stretching algorithm is proposed to minimize the intraclass distance of facial skin and maximize the dynamic range of facial texture distribution. Compared with the existing methods, the proposed method can more effectively eliminate the redundant information of facial skin and illumination. Extensive experiments show that the proposed method has superior performance in normalizing illumination variation and enhancing facial texture features for illumination-insensitive face recognition.

What is the Right Illumination Normalization for Face Recognition?

In this paper, we investigate the effect of some illumination normalization techniques on a simple linear subspace face recognition model using two distance metrics on three challenging, yet interesting databases. The research takes the form of experimentation and analysis in which five illumination normalization techniques were compared and analyzed using two different distance metrics. The performances and execution times of the various techniques were recorded and measured for accuracy and efficiency. The illumination normalization techniques were Gamma Intensity Correction (GIC), discrete Cosine Transform (DCT), Histogram Remapping using Normal distribution (HRN), Histogram Remapping using Log-normal distribution (HRL), and Anisotropic Smoothing technique (AS). Results showed that improved recognition rate was obtained when the right preprocessing method is applied to the appropriate database using the right classifier.

Illumination normalization using fuzzy filter in DCT domain for face recognition

We develop a new approach of illumination normalization for face recognition under varying lighting conditions. The effect of illumination variations is in decreasing order over low-frequency discrete cosine transform (DCT) coefficients. The proposed approach is expected to nullify the effect of illumination variations as well as to preserve the low-frequency details of a face image in order to achieve a good recognition performance. This has been accomplished by using a fuzzy filter applied over the low-frequency DCT (LFDCT) coefficients. The ‘simple classification technique’ (k-nearest neighbor classification) is used to establish the performance improvement by present approach of illumination normalization under high and unpredictable illumination variations. Our fuzzy filter based illumination normalization approach achieves zero error rate on Yale face database B (named as Yale B database in this work) and CMU PIE database. An excellent performance is achieved on extended Yale B database. The present approach of illumination normalization is also tested on Yale face database which comprises of illumination variations together with expression variations and misalignment. Significant reduction in the error rate is achieved by the present approach on this database as well. These results establish the superiority of the proposed approach of illumination normalization, over the existing ones.

New Fuzzy-Based Retinex Method for the Illumination Normalization of Face Recognition

We propose a new illumination normalization for face recognition which robust in relation to the illumination variations on mobile devices. This research is novel in the following five ways when compared to previous works: (i) a new fuzzy-based Retinex method is proposed for illumination normalization; (ii) the performance of face recognition is enhanced by determining the optimal parameter of Retinex filtering based on fuzzy logic; (iii) the output of the fuzzy membership function is adaptively determined based on the mean and standard deviations of the grey values of the detected face region; (iv) through the comparison of various defuzzification methods in terms of the accuracy of face recognition, one optimal method was selected; (v) we proved the validations of the proposed method by testing it with various face recognition methods. Experimental results showed that the accuracy of the face recognition with the proposed method was enhanced compared to previous ones.

Multiscale illumination normalization for face recognition using dual-tree complex wavelet transform in logarithm domain

A new illumination normalization approach based on multiscale dual-tree complex wavelet transform (DT-CWT) is presented for variable lighting face recognition. In our method, low resolution wavelet coefficients are truncated to minimize variations under different lighting conditions. On the other hand, the coefficients in the directional subbands are used for extracting the invariant facial features. In order to reduce the halo artifacts, new conduction function is presented which can smooth the high-frequency illumination discontinuities while keeping most of the facial features unimpaired. The histogram remapping technique is employed on the normalized image which remaps the histogram into normal distribution and thus it can improve the recognition performance. Experimental results on the Yale B and CMU PIE databases show that the proposed method can achieve satisfactory performance for recognizing face images under large illumination variations.

基于微观本义复原的人脸图像光照正规化

基于微观本义复原的人脸图像光照正规化