Robust Face Recognition Using Kernel Collaborative Representation and Multi-scale Local Binary Patterns

2016 ◽  
pp. 253-271
Author(s):  
Muhammad Khurram Shaikh ◽  
Muhammad Atif Tahir ◽  
Ahmed Bouridane
Keyword(s):  
Face Recognition ◽  
Local Binary Patterns ◽  
Collaborative Representation ◽  
Multi Scale ◽  
Robust Face Recognition

scholarly journals Learning Multi-scale Block Local Binary Patterns for Face Recognition

2007 ◽  
pp. 828-837 ◽  
Author(s):  
Shengcai Liao ◽  
Xiangxin Zhu ◽  
Zhen Lei ◽  
Lun Zhang ◽  
Stan Z. Li
Keyword(s):  
Face Recognition ◽  
Local Binary Patterns ◽  
Multi Scale

Feature Representation Method Based on Kirsch Masks Filter for Face Recognition

2014 ◽  
Vol 989-994 ◽  
pp. 4209-4212
Author(s):  
Zhao Kui Li ◽  
Yan Wang
Keyword(s):  
Face Recognition ◽  
Local Binary Patterns ◽  
Feature Representation ◽  
Linear Discriminant ◽  
Robust Coding ◽  
Gabor Features ◽  
Robust Face Recognition ◽  
Representation Method ◽  
Low Dimensional ◽  
Direction Image

In this paper, a feature representation method based on Kirsch masks filter for face recognition is proposed. We firstly obtain eight direction images by performing Kirsch masks filter. For each direction image, the low-dimensional feature vector is computed by Linear Discriminant Analysisis. Then, a fusion strategy is used to combine different direction image according to their respective salience. Experimental results show that our methods significantly outperform popular methods such as Gabor features, Local Binary Patterns, Regularized Robust Coding (RRC), and achieve state-of-the-art performance for difficult problems such as illumination and occlusion-robust face recognition.

scholarly journals Boosting Texture-Based Classification by Describing Statistical Information of Gray-Levels Differences

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1048 ◽  
Author(s):  
Óscar García-Olalla ◽  
Laura Fernández-Robles ◽  
Enrique Alegre ◽  
Manuel Castejón-Limas ◽  
Eduardo Fidalgo
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Local Binary Patterns ◽  
Statistical Information ◽  
The State ◽  
Texture Descriptor ◽  
Texture Recognition ◽  
Material Recognition ◽  
Multi Scale ◽  
Hit Rate

This paper presents a new texture descriptor booster, Complete Local Oriented Statistical Information Booster (CLOSIB), based on statistical information of the image. Our proposal uses the statistical information of the texture provided by the image gray-levels differences to increase the discriminative capability of Local Binary Patterns (LBP)-based and other texture descriptors. We demonstrated that Half-CLOSIB and M-CLOSIB versions are more efficient and precise than the general one. H-CLOSIB may eliminate redundant statistical information and the multi-scale version, M-CLOSIB, is more robust. We evaluated our method using four datasets: KTH TIPS (2-a) for material recognition, UIUC and USPTex for general texture recognition and JAFFE for face recognition. The results show that when we combine CLOSIB with well-known LBP-based descriptors, the hit rate increases in all the cases, introducing in this way the idea that CLOSIB can be used to enhance the description of texture in a significant number of situations. Additionally, a comparison with recent algorithms demonstrates that a combination of LBP methods with CLOSIB variants obtains comparable results to those of the state-of-the-art.

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