Illumination Normalization Techniques Research Articles

Illumination normalization techniques for makeup-invariant face recognition

Face recognition research in unconstrained environments has focused mainly on the three classical causes of variation, i.e., Pose, Illumination, and Expression (PIE). Another recently identified issue is facial makeup that poses a significant challenge to face recognition systems. We believe that applying makeup can cause changes similar to the ones observed due to illumination variation. Therefore, in this paper, we have investigated the effectiveness of illumination normalization techniques for decreasing the variations caused by makeup in a face recognition system. First, we apply photometric illumination normalization techniques with their parameters adjusted for face recognition. Next, we extract facial features using texture-based feature extraction methods and perform face recognition using Support Vector Machines. Experiments carried out on both constrained and unconstrained databases clearly show that illumination normalization techniques improve face recognition results.

Plastic Surgery Face Recognition USING Photometric based illumination normalization Techniques and Gabor faces (Dept. E. (Electronics))

Plastic Surgery Face Recognition USING Photometric based illumination normalization Techniques and Gabor faces (Dept. E. (Electronics))

Histogram equalization and its variants on principal component analysis based face recognition system

Principal Component Analysis (PCA) is an appearance-based feature extraction technique that is widely used in facial recognition system which suffers from illumination conditions, thus the challenge of knowing which illumination control methods to be used in facial recognition system based on PCA algorithm is very important. This paper compared the performance of Principal Component Analysis based face recognition using illumination normalization techniques: Histogram Equalization (HE), Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The evaluation of the performance of the developed system was done comparatively in terms of false acceptance rate, false rejection rate and recognition rate. The best recognition rate of 83.67% was achieved in AHE for ORL, the effective false acceptance rate (FAR) of 20% was recorded in CLAHE with FERET database and also effective false recognition rate (FRR) of 10% was obtained in CLAHE for FERET database.Keywords: Histogram Equalization, Adaptive Histogram Equalization, Contrast Adaptive Limited Histogram Equalization, Principal Component Analysis

An adaptive illumination normalization using non-linear regression for robust person identification under varying illuminations

The precision of person recognition using facial images in complex illumination conditions deteriorates severely. Various illumination normalization and compensation techniques alleviate the classification performance by linear and static modeling of illumination variations thus result in inappropriate illumination normalization. Here, the authors present novel and adaptive illumination normalization method with the help of kernel extreme learning machine (KELM). Few low frequency discrete-Cosine-transform (DCT) coefficients are used as input to KELM for learning the non-linear regression model for illumination variations. The trained KELM is used to find the illumination variations present in the input face image which is represented by the regression value. Depending upon the regression value corresponding to the illumination variations in the test image, the number of low frequency DCT coefficients is selected adaptively. The selected low frequency DCT coefficients are processed by a fuzzy filter. The processed face images are classified by KELM with polynomial kernel. The evaluation of the proposed illumination normalization method is conducted on Extended Yale B face database. Zero, 0.19 and 1.11 percent error rate on subset 3, 4 and 5, respectively, of this database proves the efficacy of the proposed method.

Low Resolution Face Recognition System Performance for Basic Feature Space Methods with Various Illumination Normalization Techniques

The Face recognition research identified with the field of automated surveillance systems in real life application has pulled in more consideration today with extensive use of vision cameras in biometrics and surveillance applications, globally. The small size images or poor quality images are generally Low Resolution images. Authenticating faces, with variation in pose, illumination, disguise and more, from such Low-resolution images, is the main purpose of Low Resolution Face Recognition (LR FR) system. We have assessed the LR FR system for various basic feature space techniques like PCA, LDA and Fisherface. Different illumination normalization techniques are applied on the cropped Yale face database prior to feature extraction and identification. In our work the low-resolution images, of size 32x32, are the down-sampled versions high-resolution facial images from cropped Yale face database. Our experiment demonstrates the encouraging performance, with recognition accuracy as 97.43%, on Low Resolution, Low quality face images.

Gray level run length matrix based on various illumination normalization techniques for texture classification

Texture classification under varying illumination conditions is one of the most important challenges. This paper presents a new texture classification approach by taking the combinations of robust illumination normalization techniques applied on gray level run length matrix (GLRLM) for texture features extraction. The purpose of selecting the GRLM, as texture descriptor is that, it extracts information of an image from its gray level runs. A set of consecutive, collinear picture points having the same gray level values is considered as a gray level run. The textured materials usually go through a deep change in their images with variations in illumination and camera pose. For instance, keeping all the parameters fixed but just changing the scale and rotation can result in a completely new texture. Hence, change in gray level values also occurred. Dealing with these variations successfully by utilizing GRLM descriptor for texture classification is the main purpose of this paper. In the suggested approach, 2D wavelet, Tan and Triggs (TT) normalization methods are employed to compensate illumination variations. Experimental results on the Brodatz, VisTex, STex and ALOT databases show that the suggested approach improves the performance significantly as compared to the classical GLRLM descriptor.

A LITERATURE SURVEY ON VARIOUS ILLUMINATION NORMALIZATION TECHNIQUES FOR FACE RECOGNITION WITH FUZZY K NEAREST NEIGHBOUR CLASSIFIER

The face recognition is popular in video surveillance, social networks and criminal identifications nowadays. The performance of face recognition would be affected by variations in illumination, pose, aging and partial occlusion of face by Wearing Hats, scarves and glasses etc. The illumination variations are still the challenging problem in face recognition. The aim is to compare the various illumination normalization techniques. The illumination normalization techniques include: Log transformations, Power Law transformations, Histogram equalization, Adaptive histogram equalization, Contrast stretching, Retinex, Multi scale Retinex, Difference of Gaussian, DCT, DCT Normalization, DWT, Gradient face, Self Quotient, Multi scale Self Quotient and Homomorphic filter. The proposed work consists of three steps. First step is to preprocess the face image with the above illumination normalization techniques; second step is to create the train and test database from the preprocessed face images and third step is to recognize the face images using Fuzzy K nearest neighbor classifier. The face recognition accuracy of all preprocessing techniques is compared using the AR face database of color images.

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.

Generalized regression neural network trained preprocessing of frequency domain correlation filter for improved face recognition and its optical implementation

The paper proposes an improved strategy for face recognition using correlation filter under varying lighting conditions and occlusion where spatial domain preprocessing is carried out by two convolution kernels. The first convolution kernel is a contour kernel for emphasizing high frequency components of face image and the other kernel is a smoothing kernel used for minimization of noise those may arise due to preprocessing. The convolution kernels are obtained by training a generalized regression neural network using enhanced face features. Face features are enhanced by conventional principal component analysis. The proposed method reduces the false acceptance rate and false rejection rate in comparison to other standard correlation filtering techniques. Moreover, the processing is fast when compared to the existing illumination normalization techniques. A scheme of hardware implementation of all optical correlation technique is also suggested based on single spatial light modulator in a beam folding architecture. Two benchmark databases YaleB and PIE are used for performance verification of the proposed scheme and the improved results are obtained for both illumination variations and occlusions in test face images.

Comparative Analysis of various Illumination Normalization Techniques for Face Recognition

The change in facial appearance due to illumination variation degrades face recognition systems performance considerably. In this paper, various states of art illumination normalization techniques have been explained and compared. The classification of the image recognition has been done using artificial neural networks (ANN). We have compared four illumination normalization methods which are (1) discrete cosine transform (DCT) with rescaling of low frequency coefficients (2) discrete cosine transform (DCT) with discarding of low frequency coefficients (3) homomorphic filtering (HF) (4) gamma intensity correction (GIC). These methods are evaluated and compared on Yale and Yale B Faces databases.