face recognition using the weber local descriptor

Face Recognition Using the Weber Local Descriptor

作者： Dayi Gong

Shutao Li

Yin Xiang

讲解人：余文倩

作者简介

龚大义湖南大学电气与信息工程学院李树涛研究生[email protected]

mailto:[email protected]

作者简介

李树涛湖南大学教授，博士生导师电气与信息工程学院[email protected]

主要研究方向 :图像处理信息融合压缩感知稀疏表示模式识别机器学习


作者简介

向荫湖南大学电气与信息工程学院李树涛研究生[email protected]


文章出处

Publication Pattern Recognition (ACPR), 2011 First Asian Conference on Date 28-

28 Nov. 2011 ， IEEE.

References J. Chen, S. Shan, C. He, et al. “WLD: a robust local image

descriptor,”IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, pp.1705–1720, September 2010

Abstract

This paper presents a face recognition method using the Weber Local Descriptor (WLD).

The authors divide face images into a set of sub-regions and extract their WLD features respectively.

They introduce the Sobel descriptor to obtain the orientation component.

The experimental results over ORL and Yale face database verify the effectiveness of our method.

文章结构

• Abstract

• Introduction

• The Extraction of WLD

• Face Recognition Method

• Experiments

• Conclusion

讲解提纲

• 人脸识别

• WLD

• 基于 WLD 的人脸识别

• 实验结果分析

• 结论

人脸识别简介

• 人脸识别：特指利用分析比较人脸视觉特征信息进行身份鉴别的计算机技术。

人脸识别流程

人脸识别

图像预处理

特征提取与选择

分类

直方图均衡化

中值滤波

基于子空间学习

贝叶斯

最近邻线性回归

基于几何特征

基于模板匹配

灰度拉升

LDAPCA

流行学习核方法

神经网络

讲解提纲

• 人脸识别

• WLD



• 结论

A. Differential Excitation

The Extraction of WLD

B. OrientationSobel operator

C.WLD Histogram

Sobel Operator

图像处理算子之一，主要用于边缘检测它是一种离散性差分算子，用来运算图像

亮度函数的梯度之近似值在图像的任何一点使用此算子，通过 3×3

模板作为核与图像中的每个像素点做卷积和运算，然后选取合适的阈值以提取边缘。

传统的 Sobel 算子

A. 检测水平边缘 B. 检测垂直边缘-1 0 1

-2 0 2

-1 0 1

1 2 1

0 0 0

-1 -2 -1

A1 A2 A3

A4 (X,Y) A5

A6 A7 A8

传统的 Sobel 算子

与图像做卷积分别得到横向与纵向的亮度差分近似值

图像的每一个像素的横向及纵向梯度近似值可以用一下的公式结合，来计算梯度的大小

𝐺 𝑋=[− 10+1− 20+2− 10+1]∗ 𝐴 ,𝐺𝑦=[+1+2+1

000−1+2+1 ]∗𝐴

𝐺=√𝐺𝑥2+𝐺𝑦

2

Sobel Operator

Why Sobel Operator

In the original methods of WLD, the gradient information is extracted by the two neighboring pixels in vertical direction, and another two in the horizontal direction of current pixel. It is easy disrupted by noises.

The convolution template of Sobel operator with different weights is used to suppress the noise.

So the Sobel operator is more appropriate to extract the gradient orientation.

A. Differential excitation

The Extraction of WLD

B.OrientationSobel operator

C.WLD Histogram

讲解提纲

• 人脸识别

• WLD



• 结论

A. Preprocessing with Gaussian filter —to make the face image smoother

Face Recognition Method

= 𝐺 (𝑥 , 𝑦 , 𝛿 )= 12𝜋 𝛿2 exp(−

𝑥2+𝑦 2

2𝛿2 )

B. Feature extractionThe face images are divided into a set of sub-regions. Feature extraction is accomplished by obtaining the WLD histogram feature

of each sub-image.


C. Decision fusionTo improve the performance of the recognition scheme, all recognition

results of the sub-images are dealt with by decision fusion through voting as:


𝑉=max𝑖𝐹 𝑖 (𝑣 𝑗 ) , 𝑗=0,1 ,…,𝑛


A CB

讲解提纲

• 人脸识别

• WLD



• 结论

A. Experiments on effect of the different parameters

D=TxN

Experiments

A. Experiments on effect of the different parameters

Divided into X*Y sub-regions

Experiments

Experiments

B. The comparison of WLD,LBP and LTP

Experiments

C. Comparison with different methods

讲解提纲

• 人脸识别

• WLD



• 结论

Conclusion

In this paper, we have presented a new face recognition algorithm based on WLD, which makes a contribution to improve the recognition accuracy.

Experimental results show that the WLD feature has a powerful representation in face recognition, which is robust to variations in facial expression, illumination condition, pose, partial occlusions etc.

In the future, we will investigate to fuse WLD with other effective features to make further improvement in face recognition field.

Thank you!

face recognition using the weber local descriptor

Documents