scholarly journals Efficient Gabor Phase Based Illumination Invariant for Face Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Chunnian Fan ◽  
Shuiping Wang ◽  
Hao Zhang
Keyword(s):  
Face Recognition ◽  
Extraction Method ◽  
Experimental Results ◽  
Gabor Wavelet ◽  
Image Transformation ◽  
Face Database ◽  
Illumination Variation ◽  
Illumination Invariant ◽  
Face Images

This paper presents a novel Gabor phase based illumination invariant extraction method aiming at eliminating the effect of varying illumination on face recognition. Firstly, It normalizes varying illumination on face images, which can reduce the effect of varying illumination to some extent. Secondly, a set of 2D real Gabor wavelet with different directions is used for image transformation, and multiple Gabor coefficients are combined into one whole in considering spectrum and phase. Lastly, the illumination invariant is obtained by extracting the phase feature from the combined coefficients. Experimental results on the Yale B and the CMU PIE face database show that our method obtained a significant improvement over other related methods for face recognition under large illumination variation condition.

Research on Importance of Texture Information in Face Recognition

2013 ◽  
Vol 278-280 ◽  
pp. 1193-1196 ◽  
Author(s):  
Yong Gao Jin ◽  
Cheng Zhe Xu
Keyword(s):  
Face Recognition ◽  
Face Image ◽  
Experimental Results ◽  
Face Database ◽  
Illumination Variation ◽  
Illumination Normalization ◽  
Skin Texture ◽  
Shape Information ◽  
Texture Information

This paper presents importance of skin texture information in face recognition. To this end, we perform the illumination normalization on face image in order to extract texture information unaffected by illumination variation. And then apply mask image on each illumination normalized face image to obtain the corresponding texture data, which hardly contain the shape information. Face recognition experiments are carried out by using texture data. Experimental results on Yale face database B and CMU PIE database show that the texture information has considerable ability to distinguish subjects in face recognition.

scholarly journals Template Based Pose and Illumination Invariant Face Recognition

2020 ◽  
Vol 8 (5) ◽  
pp. 3220-3229
Keyword(s):  
Face Recognition ◽  
Gabor Wavelet ◽  
Mathematical Methods ◽  
Query Image ◽  
Illumination Invariant ◽  
Face Images ◽  
The Face ◽  
Similar Images ◽  
Proper Face ◽  
The Impact

This article presents a method “Template based pose and illumination invariant face recognition”. We know that pose and Illumination are important variants where we cannot find proper face images for a given query image. As per the literature, previous methods are also not accurately calculating the pose and Illumination variants of a person face image. So we concentrated on pose and Illumination. Our System firstly calculates the face inclination or the pose of the head of a person with various mathematical methods. Then Our System removes the Illumination from the image using a Gabor phase based illumination invariant extraction strategy. In this strategy, the system normalizes changing light on face images, which can decrease the impact of fluctuating Illumination somewhat. Furthermore, a lot of 2D genuine Gabor wavelet with various orientations is utilized for image change, and numerous Gabor coefficients are consolidated into one entire in thinking about spectrum and phase. Finally, the light invariant is acquired by separating the phase feature from the consolidated coefficients. Then after that, the obtained Pose and illumination invariant images are convolved with Gabor filters to obtain Gabor images. Then templates will be extracted from these Gabor images and one template average is generated. Then similarity measure will be performed between query image template average and database images template averages. Finally the most similar images will be displayed to the user. Exploratory results on PubFig database, Yale B and CMU PIE face databases show that our technique got a critical improvement over other related strategies for face recognition under enormous pose and light variation conditions.

scholarly journals Illumination invariant face recognition using dual-tree complex wavelet transform in logarithm domain

2019 ◽  
Vol 70 (2) ◽  
pp. 113-121
Author(s):  
Guang Yi Chen ◽  
Tien D. Bui ◽  
Adam Krzyzak
Keyword(s):  
Face Recognition ◽  
Wavelet Transform ◽  
Collaborative Representation ◽  
Face Database ◽  
Complex Wavelet Transform ◽  
Illumination Invariant ◽  
Face Images ◽  
The Face ◽  
Zero Values ◽  
Complex Wavelet

Abstract In this article, we develop a new algorithm for illumination invariant face recognition. We first transform the face images to the logarithm domain, which makes the dark regions brighter. We then use dual-tree complex wavelet transform to generate face images that are approximately invariant to illumination changes and use collaborative representation-based classifier to classify the unknown faces to one known class. We set the approximation sub-band and the highest two DTCWT coefficient sub-bands to zero values before the inverse DTCWT transform is performed. Experimental results demonstrate that our proposed method improves upon a few existing methods under both the noise-free and noisy environments for the Extended Yale Face Database B and the CMU-PIE face database.

scholarly journals Diagonal Symmetric Pattern-Based Illumination Invariant Measure for Severe Illumination Variation Face Recognition

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 63202-63213 ◽  
Author(s):  
Changhui Hu ◽  
Fei Wu ◽  
Jian Yu ◽  
Xiaoyuan Jing ◽  
Xiaobo Lu ◽  
...  
Keyword(s):  
Face Recognition ◽  
Invariant Measure ◽  
Illumination Variation ◽  
Illumination Invariant ◽  
Symmetric Pattern

KERNEL SUPERVISED DISCRIMINANT PROJECTION AND ITS APPLICATION FOR FACE RECOGNITION

2013 ◽  
Vol 27 (02) ◽  
pp. 1356003 ◽  
Author(s):  
JIANGUO WANG
Keyword(s):  
Face Recognition ◽  
Experimental Results ◽  
Subspace Method ◽  
Subspace Analysis ◽  
Face Images ◽  
Discriminant Projection ◽  
Kernel Mapping ◽  
Discriminant Information

Subspace analysis is an effective approach for face recognition. In this paper, a novel subspace method, called kernel supervised discriminant projection (KSDP), is proposed for face recognition. In the proposed method, not only discriminant information with intrinsic geometric relations is preserved in subspace, but also complex nonlinear variations of face images are represented by nonlinear kernel mapping. Extensive experiments are performed to test and evaluate the new algorithm. Experimental results on three popular benchmark databases, FERET, Yale and AR, demonstrate the effectiveness of the proposed method, KSDP.

scholarly journals Online Training for Face Recognition System Using Improved PCA

2011 ◽  
Vol 2 (2) ◽  
pp. 1303
Author(s):  
Widodo Budiharto
Keyword(s):  
Face Recognition ◽  
Stereo Vision ◽  
Indoor Environment ◽  
Online Training ◽  
Recognition System ◽  
Service Robot ◽  
Challenging Problem ◽  
Face Database ◽  
Face Images ◽  
Face Recognition System

The variation in illumination is one of the main challenging problem for face recognition. It has been proven that in face recognition, differences caused by illumination variations are more significant than differences between individuals. Recognizing face reliably across changes in pose and illumination using PCA has proved to be a much harder problem because eigenfaces method comparing the intensity of the pixel. To solve this problem, this research proposes an online face recognition system using improved PCA for a service robot in indoor environment based on stereo vision. Tested images are improved by generating random values for varying the intensity of face images. A program for online training is also developed where the tested images are captured real-time from camera. Varying illumination in tested images will increase the accuracy using ITS face database which its accuracy is 95.5 %, higher than ATT face database’s as 95.4% and Indian face database’s as 72%. The results from this experiment are still evaluated to be improved in the future.

scholarly journals High Performance Human Face Recognition using Gabor Based Pseudo Hidden Markov Model

2013 ◽  
Vol 4 (1) ◽  
pp. 81-102 ◽  
Author(s):  
Arindam Kar ◽  
Debotosh Bhattacharjee ◽  
Mita Nasipuri ◽  
Dipak Kumar Basu ◽  
Mahantapas Kundu
Keyword(s):  
Face Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
High Performance ◽  
Hidden Markov ◽  
Gabor Wavelet ◽  
Data Set ◽  
Scanning Method ◽  
Face Images ◽  
Gabor Wavelet Transformation

This paper introduces a novel methodology that combines the multi-resolution feature of the Gabor wavelet transformation (GWT) with the local interactions of the facial structures expressed through the Pseudo Hidden Markov Model (PHMM). Unlike the traditional zigzag scanning method for feature extraction a continuous scanning method from top-left corner to right then top-down and right to left and so on until right-bottom of the image i.e., a spiral scanning technique has been proposed for better feature selection. Unlike traditional HMMs, the proposed PHMM does not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the PHMM used to extract facial bands and automatically select the most informative features of a face image. Thus, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. Again with the use of most informative pixels rather than the whole image makes the proposed method reasonably faster for face recognition. This method has been successfully tested on frontal face images from the ORL, FRAV2D, and FERET face databases where the images vary in pose, illumination, expression, and scale. The FERET data set contains 2200 frontal face images of 200 subjects, while the FRAV2D data set consists of 1100 images of 100 subjects and the full ORL database is considered. The results reported in this application are far better than the recent and most referred systems.

scholarly journals A State of the art Survey on Face Recognition Methods

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
State Of The Art ◽  
Hidden Markov ◽  
Gabor Wavelet ◽  
Support Vector ◽  
Illumination Variation ◽  
Software Application ◽  
Pose Variation ◽  
Wavelet Pca

Face Recognition is an efficient technique and one of the most liked biometric software application for the identification and verification of specific individual in a digital image by analysing and comparing patterns. This paper presents a survey on well-known techniques of face recognition. The primary goal of this review is to observe the performance of different face recognition algorithms such as SVM (Support Vector Machine), CNN (Convolutional Neural Network), Eigenface based algorithm, Gabor Wavelet, PCA (Principle Component Analysis) and HMM (Hidden Markov Model). It presents comparative analysis about the efficiency of each algorithm. This paper also figure out about various face recognition applications used in real world and face recognition challenges like Illumination Variation, Pose Variation, Occlusion, Expressions Variation, Low Resolution and Ageing in brief. Another interesting component covered in this paper is review of datasets available for face recognition. So, must needed survey of many recently introduced face recognition aspects and algorithms are presented.

Illumination Invariant Face Recognition

2018 ◽  
pp. 58-79 ◽  
Author(s):  
Chi Ho Chan ◽  
Xuan Zou ◽  
Norman Poh ◽  
Josef Kittler
Keyword(s):  
Face Recognition ◽  
Visible Light ◽  
Illumination Variation ◽  
Illumination Invariant ◽  
Illumination Problem ◽  
Passive Techniques ◽  
Face Appearance

Illumination variation is one of the well-known problems in face recognition, especially in uncontrolled environments. This chapter presents an extensive and up-to-date survey of the existing techniques to address this problem. This survey covers the conventional passive techniques that attempt to solve the illumination problem by studying the visible light images, in which face appearance has been altered by varying illumination, as well as the active techniques that aim to obtain images of face modalities invariant to environmental illumination.

ILLUMINATION INVARIANT FACE RECOGNITION USING FABEMD DECOMPOSITION WITH DETAIL MEASURE WEIGHT

2011 ◽  
Vol 25 (08) ◽  
pp. 1261-1273 ◽  
Author(s):  
HENGXIN CHEN ◽  
Y. Y. TANG ◽  
BIN FANG ◽  
JING WEN
Keyword(s):  
Face Recognition ◽  
Empirical Mode Decomposition ◽  
High Frequency ◽  
Local Area ◽  
Face Database ◽  
Illumination Variation ◽  
Intrinsic Structure ◽  
Mode Decomposition ◽  
The Face ◽  
Bidimensional Empirical Mode Decomposition

With varying illumination conditions, facial features obtained from images are distorted nonlinearly by variant lighting intensity and direction, so face recognition becomes very difficult. According to the "common assumption", illumination varies slowly and the face intrinsic feature (including 3D surface and reflectance) varies rapidly in local area, we can then consider high frequency features that represent the face intrinsic structure. FABEMD8 (Fast and Adaptive Bidimensional Empirical Mode Decomposition) is a fast and adaptive method of BEMD22 (Bidimensional Empirical Mode Decomposition), and not using time-consuming plane interpolation computation, it can decompose the image into multilayer high frequency images representing detail features and low frequency images representing analogy features. But we cannot make a quantitative analysis of how many detail features can be used to eliminate illumination variation. So we propose two measures to quantify the detail features, and with these measure weights, we can activitate FABEMD based multilayer detail images matching for face recognition under varying illumination. With PCA, the experiments based on Yale face database B and MU PIE face database show that the method proposed in this paper can get remarkable performance.

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