Fusing hierarchical multi-scale local binary patterns and virtual mirror samples to perform face recognition

2015 ◽  
Vol 26 (8) ◽  
pp. 2013-2026 ◽  
Author(s):  
Zi Liu ◽  
Xiaoning Song ◽  
Zhenmin Tang
Keyword(s):  
Face Recognition ◽  
Local Binary Patterns ◽  
Multi Scale

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

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.

scholarly journals An Integrated Technique for Face Sketch Recognition Using DCNN

2019 ◽  
Vol 8 (10) ◽  
pp. 3133-3140
Keyword(s):  
Face Recognition ◽  
Principal Component ◽  
Local Binary Patterns ◽  
Image Features ◽  
Local Descriptor ◽  
Multi Scale ◽  
Face Sketch Recognition ◽  
Tchebichef Moments ◽  
The Face ◽  
Integrated Technique

Face Recognition (FR) is considered as one of the chief use in the investigation of criminals. In the majority of the cases, information about the criminal is not available. In such situations, sketch artist draw the sketch of the guess with the oral explanation provided by the eyewitness. These sketches can then be matched manually against mug shot photos. This process is time-consuming. Hence there require a method that efficiently goes with composite sketches to the gallery of mug shot databases. Thus the proposed system uses a scheme for matching composite sketch and photo images, photo image features are extracted and fused to train the system. Composite Sketch feature is matched with face photo images. Feature extraction (FE) is done using Multi-Scale Local Binary Patterns (MLBP) Tchebichef Moments and Multiscale Circular Weber Local Descriptor (MCWLD), Principal Component Analysis (PCA) is used for fusion of extracted features, DCNN used as a classifier to recognize the face. The experiments are conducted using PRIP-HDC dataset and the proposed system gives good accuracy in face recognition.

Face Recognition Based on Local Binary Patterns with Threshold

2010 ◽  
Author(s):  
Jun Meng ◽  
Yumao Gao ◽  
Xiukun Wang ◽  
Tsauyoung Lin ◽  
Jianying Zhang
Keyword(s):  
Face Recognition ◽  
Local Binary Patterns

MULTI-SCALE GRADIENT INVARIANT FOR FACE RECOGNITION UNDER VARYING ILLUMINATION

2012 ◽  
Vol 26 (08) ◽  
pp. 1256016 ◽  
Author(s):  
BIN XU ◽  
YUAN YAN TANG ◽  
BIN FANG ◽  
ZHAO WEI SHANG
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Image Noise ◽  
Scale Analysis ◽  
Image Gradient ◽  
Multi Scale ◽  
Novel Approach ◽  
Gradient Domain ◽  
Inherent Structure ◽  
Multi Scale Analysis

In this paper, a novel approach derived from image gradient domain called multi-scale gradient faces (MGF) is proposed to abstract multi-scale illumination-insensitive measure for face recognition. MGF applies multi-scale analysis on image gradient information, which can discover underlying inherent structure in images and keep the details at most while removing varying lighting. The proposed approach provides state-of-the-art performance on Extended YaleB and PIE: Recognition rates of 99.11% achieved on PIE database and 99.38% achieved on YaleB which outperforms most existing approaches. Furthermore, the experimental results on noised Yale-B validate that MGF is more robust to image noise.

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