Face Recognition Using the Weber Local Descriptor
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Transcript of 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
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
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
• 基于 WLD 的人脸识别
• 实验结果分析
• 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
Sobel Operator
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
• 基于 WLD 的人脸识别
• 实验结果分析
• 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.
Face Recognition Method
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:
Face Recognition Method
𝑉=max𝑖𝐹 𝑖 (𝑣 𝑗 ) , 𝑗=0,1 ,…,𝑛
Face Recognition Method
A CB
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
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
• 基于 WLD 的人脸识别
• 实验结果分析
• 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!