Robust face recognition with multi-scale Weber local descriptor

2017 ◽  
Vol 15 (05) ◽  
pp. 1750052 ◽  
Author(s):  
Guangchao Yang ◽  
Bin Fang ◽  
Yuan Yan Tang
Keyword(s):  
Face Recognition ◽  
Poor Quality ◽  
Chi Square ◽  
Local Descriptor ◽  
Multi Scale ◽  
Weber Local Descriptor ◽  
Novel Approach ◽  
Robust Face Recognition ◽  
Voting Strategy ◽  
Nearest Neighborhood

This paper presents a simple and novel approach for robust face recognition. Extended multi-scale Weber local descriptor based on unfixed size patches is proposed to obtain micro and macro patterns. Then the recognition is performed using nearest neighborhood method based on Chi-square distance. Voting strategy is utilized in classification to reduce the impacts of the poor quality regions in face image caused by pose, illumination variations. Experiments on CMU-PIE, FERET and AR databases show that the proposed method has higher recognition accuracy in comparison with traditional methods.

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.

Pose-invariant face recognition using facial landmarks and Weber local descriptor

2015 ◽  
Vol 84 ◽  
pp. 78-88 ◽  
Author(s):  
Zheng Zhang ◽  
Long Wang ◽  
Qi Zhu ◽  
Shu-Kai Chen ◽  
Yan Chen
Keyword(s):  
Face Recognition ◽  
Local Descriptor ◽  
Facial Landmarks ◽  
Weber Local Descriptor

scholarly journals Face Recognition and Drunk Classification Using Infrared Face Images

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Gabriel Hermosilla ◽  
José Luis Verdugo ◽  
Gonzalo Farias ◽  
Esteban Vera ◽  
Francisco Pizarro ◽  
...  
Keyword(s):  
Face Recognition ◽  
Promising Result ◽  
Gaussian Mixture ◽  
Local Descriptor ◽  
Linear Discriminant ◽  
Face Images ◽  
The Face ◽  
Robust Face Recognition ◽  
Thermal Face ◽  
Two Stages

The aim of this study is to propose a system that is capable of recognising the identity of a person, indicating whether the person is drunk using only information extracted from thermal face images. The proposed system is divided into two stages, face recognition and classification. In the face recognition stage, test images are recognised using robust face recognition algorithms: Weber local descriptor (WLD) and local binary pattern (LBP). The classification stage uses Fisher linear discriminant to reduce the dimensionality of the features, and those features are classified using a classifier based on a Gaussian mixture model, creating a classification space for each person, extending the state-of-the-art concept of a “DrunkSpace Classifier.” The system was validated using a new drunk person database, which was specially designed for this work. The main results show that the performance of the face recognition stage was 100% with both algorithms, while the drunk identification saw a performance of 86.96%, which is a very promising result considering 46 individuals for our database in comparison with others that can be found in the literature.

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