Illumination and Rotation Invariant Texture Representation for Face Recognition

2020 ◽  
Vol 10 (2) ◽  
pp. 58-69
Author(s):  
Medha Kudari ◽  
Shivashankar S. ◽  
Prakash S. Hiremath
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Texture Features ◽  
Galois Field ◽  
Rotation Invariance ◽  
Invariance Property ◽  
Rotation Invariant ◽  
Texture Representation ◽  
Novel Approach ◽  
Local Gradient

This article presents a novel approach for illumination and rotation invariant texture representation for face recognition. A gradient transformation is used as illumination invariance property and a Galois Field for the rotation invariance property. The normalized cumulative histogram bin values of the Gradient Galois Field transformed image represent the illumination and rotation invariant texture features. These features are further used as face descriptors. Experimentations are performed on FERET and extended Cohn Kanade databases. The results show that the proposed method is better as compared to Rotation Invariant Local Binary Pattern, Log-polar transform and Sorted Local Gradient Pattern and is illumination and rotation invariant.

scholarly journals HUMAN IDENTIFICATION BASED ON FACE RECOGNITION SYSTEM

2021 ◽  
Vol 25 (01) ◽  
pp. 80-91
Author(s):  
Saba K. Naji ◽  
◽  
Muthana H. Hamd ◽  
Keyword(s):  
Face Recognition ◽  
Euclidean Distance ◽  
Local Binary Pattern ◽  
Texture Features ◽  
Recognition System ◽  
Manhattan Distance ◽  
Distance Measurements ◽  
Local Ternary Pattern ◽  
Face Recognition System ◽  
Cosine Distance

Due to, the great electronic development, which reinforced the need to define people's identities, different methods, and databases to identification people's identities have emerged. In this paper, we compare the results of two texture analysis methods: Local Binary Pattern (LBP) and Local Ternary Pattern (LTP). The comparison based on comparing the extracting facial texture features of 40 and 401 subjects taken from ORL and UFI databases respectively. As well, the comparison has taken in the account using three distance measurements such as; Manhattan Distance (MD), Euclidean Distance (ED), and Cosine Distance (CD). Where the maximum accuracy of the LBP method (99.23%) is obtained with a Manhattan and ORL database, while the LTP method attained (98.76%) using the same distance and database. While, the facial database of UFI shows low quality, which is satisfied 75.98% and 73.82% recognition rates using LBP and LTP respectively with Manhattan distance.

Face Recognition by PCA and Improved LBP Fusion Algorithm

2015 ◽  
Vol 734 ◽  
pp. 562-567 ◽  
Author(s):  
En Zeng Dong ◽  
Yan Hong Fu ◽  
Ji Gang Tong
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Texture Feature ◽  
Principal Component ◽  
Texture Features ◽  
Rotation Invariant ◽  
Fusion Algorithm ◽  
Face Images ◽  
The Face ◽  
Pca Method

This paper proposed a theoretically efficient approach for face recognition based on principal component analysis (PCA) and rotation invariant uniform local binary pattern texture features in order to weaken the effects of varying illumination conditions and facial expressions. Firstly, the rotation invariant uniform LBP operator was adopted to extract the local texture feature of the face images. Then PCA method was used to reduce the dimensionality of the extracted feature and get the eigenfaces. Finally, the nearest distance classification was used to distinguish each face. The method has been accessed on Yale and ATR-Jaffe face databases. Results demonstrate that the proposed method is superior to standard PCA and its recognition rate is higher than the traditional PCA. And the proposed algorithm has strong robustness against the illumination changes, pose, rotation and expressions.

scholarly journals The Face Recognition Method Based on CS-LBP and DBN

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Kun Sun ◽  
Xin Yin ◽  
Mingxin Yang ◽  
Yang Wang ◽  
Jianying Fan
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Recognition Rate ◽  
Texture Feature ◽  
Texture Features ◽  
Face Image ◽  
Recognition Method ◽  
Traditional Methods ◽  
Face Images ◽  
The Face

At present, the face recognition method based on deep belief network (DBN) has advantages of automatically learning the abstract information of face images and being affected slightly by active factors, so it becomes the main method in the face recognition area. Because DBN ignores the local information of face images, the face recognition rate based on DBN is badly affected. To solve this problem, a face recognition method based on center-symmetric local binary pattern (CS-LBP) and DBN (FRMCD) is proposed in this paper. Firstly, the face image is divided into several subblocks. Secondly, CS-LBP is used to extract texture features of each image subblock. Thirdly, texture feature histograms are formed and input into the DBN visual layer. Finally, face classification and face recognition are completed through deep learning in DBN. Through the experiments on face databases ORL, Extend Yale B, and CMU-PIE by the proposed method (FRMCD), the best partitioning way of the face image and the hidden unit number of the DBN hidden layer are obtained. Then, comparative experiments between the FRMCD and traditional methods are performed. The results show that the recognition rate of FRMCD is superior to those of traditional methods; the highest recognition rate is up to 98.82%. When the number of training samples is less, the FRMCD has more significant advantages. Compared with the method based on local binary pattern (LBP) and DBN, the time-consuming of FRMCD is shorter.

A NOVEL TEXTURE-BASED MULTI-LINEAR ANALYSIS ALGORITHM FOR FACE RECOGNITION

2011 ◽  
Vol 11 (04) ◽  
pp. 495-508
Author(s):  
HUCHUAN LU ◽  
DONG WANG ◽  
YEN-WEI CHEN ◽  
HAO CHEN
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Texture Features ◽  
Linear Analysis ◽  
Texture Information ◽  
Linear Approach ◽  
Series Of Experiments ◽  
Facial Images ◽  
Effective Representation ◽  
Orthogonal Iteration

In this paper, a multi-linear approach based on texture features for face recognition is proposed. First, we extract fragment-based texture features of the facial images using the local binary pattern (LBP) descriptors, which capture both shape and texture information and also are robust to illumination variations. Second, we propose high-order orthogonal iteration (HOOI) algorithm that obtains optimum truncated factor-specific modes, which are not guaranteed in the standard N-mode SVD algorithm, in an iterative manner. Finally, we apply HOOI to obtain a compact and effective representation of the facial images based on the texture features. Our representation yields improved facial recognition rates relative to standard eigenface, tensorface, and other popular algorithms, especially when the facial images are confronted by a variety of viewpoints and illuminations. To evaluate the validity of our approach, a series of experiments are performed on the CMU-PIE facial databases.

scholarly journals Texture Representation and Application of Colored Spun Fabric Using Uniform Three-Structure Descriptor

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Yuan Li ◽  
Muli Liu ◽  
JunPing Liu ◽  
Yali Yang ◽  
Xue Gong
Keyword(s):  
Local Binary Pattern ◽  
Contextual Information ◽  
Texture Features ◽  
Correlation Index ◽  
Average Precision ◽  
Texture Representation ◽  
The Mean ◽  
Texture Structure ◽  
Different Color ◽  
Structure Descriptor

Abstract The local binary pattern (LBP) and its variants have shown their effectiveness in texture images representation. However, most of these LBP methods only focus on the histogram of LBP patterns, ignoring the spatial contextual information among them. In this paper, a uniform three-structure descriptor method was proposed by using three different encoding methods so as to obtain the local spatial contextual information for characterizing the nonuniform texture on the surface of colored spun fabrics. The testing results of 180 samples with 18 different color schemes indicate that the established texture representation model can accurately express the nonuniform texture structure of colored spun fabrics. In addition, the overall correlation index between texture features and sample parameters is 0.027 and 0.024, respectively. When compared with the LBP and its variants, the proposed method obtains a higher representational ability, and simultaneously owns a shorter time complexity. At the same time, the algorithm proposed in this paper enjoys ideal effectiveness and universality for fabric image retrieval. The mean Average Precision (mAP) of the first group of samples is 86.2%; in the second group of samples, the mAP of the sample with low twist coefficient is 89.6%, while the mAP of the sample with high twist coefficient is 88.5%.

scholarly journals Classification of wood defect images using local binary pattern variants

2020 ◽  
Vol 6 (1) ◽  
pp. 36
Author(s):  
Rahillda Nadhirah Norizzaty Rahiddin ◽  
Ummi Rabaah Hashim ◽  
Nor Haslinda Ismail ◽  
Lizawati Salahuddin ◽  
Ngo Hea Choon ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Wood Species ◽  
Local Binary Pattern ◽  
Rotation Invariant ◽  
Texture Representation ◽  
Accuracy Level ◽  
Extraction Processes ◽  
Uniform Lbp

This paper presents an analysis of the statistical texture representation of the Local Binary Pattern (LBP) variants in the classification of wood defect images. The basic and variants of the LBP feature set that was constructed from a stage of feature extraction processes with the Basic LBP, Rotation Invariant LBP, Uniform LBP, and Rotation Invariant Uniform LBP. For significantly discriminating, the wood defect classes were further evaluated with the use of different classifiers. By comparing the results of the classification performances that had been conducted across the multiple wood species, the Uniform LBP was found to have demonstrated the highest accuracy level in the classification of the wood defects.

scholarly journals A novel approach for Face Recognition using Local Binary Pattern

2012 ◽  
pp. 25-31
Author(s):  
Sonal R. Ahirrao ◽  
D. S. Bormane
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Recognition Rate ◽  
Global Features ◽  
Face Images ◽  
Grey Scale ◽  
Novel Approach ◽  
The Face ◽  
Scale Transformations ◽  
Micro Patterns

This paper presents Local Binary pattern (LBP) as an approach for face recognition with the use of some global features also. Face recognition has received quite a lot of attention from researchers in biometrics, pattern recognition, and computer vision communities. The idea behind using the LBP features is that the face images can be seen as composition of micro-patterns which are invariant with respect to monotonic grey scale transformations and robust to factors like ageing. Combining these micro-patterns, a global description of the face image is obtained. Efficiency and the simplicity of the proposed method allows for very fast feature extraction giving better accuracy than the other algorithms. The proposed method is tested and evaluated on ORL datasets combined with other university dataset to give a good recognition rate and 89% classification accuracy using LBP only and 98% when global features are combined with LBP. The method is also tested for real images to give good accuracy and recognition rate. The experimental results show that the method is valid and feasible.

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