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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 gdyhnu@yahoo.cn. 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 shutao_li@hnu.edu.cn 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

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Face Recognition Using the Weber Local Descriptor

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  1. Face Recognition Using the Weber Local Descriptor 作者：Dayi Gong Shutao Li Yin Xiang 讲解人：余文倩

  2. 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 • gdyhnu@yahoo.cn

  3. 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 • shutao_li@hnu.edu.cn 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

  4. 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 • Xiangyin123.happy@163.com

  5. 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

  6. 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.

  7. 文章结构 • Abstract • Introduction • The Extraction of WLD • Face Recognition Method • Experiments • Conclusion

  8. 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

  9. 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

  10. 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。

  11. 人脸识别流程 特征提取与选择 PCA LDA 神经网络 基于子空间学习 基于模板匹配 核方法 流行学习 基于几何特征 人脸识别 中值滤波 贝叶斯 灰度拉升 线性回归 直方图均衡化 最近邻 分类 图像预处理

  12. 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

  13. The Extraction of WLD A. Differential Excitation • B. Orientation • Sobel operator C.WLD Histogram

  14. SobelOperator • 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。

  15. 传统的Sobel算子 A. 检测水平边缘 B.检测垂直边缘

  16. 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

  17. SobelOperator

  18. SobelOperator

  19. SobelOperator

  20. Why SobelOperator • 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.

  21. The Extraction of WLD A. Differential excitation • B.Orientation • Sobel operator C.WLD Histogram

  22. 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

  23. Face Recognition Method Preprocessing with Gaussian filter —to make the face image smoother =

  24. Face Recognition Method B. Feature extraction • The face images are divided into a set of sub-regions. • Feature extraction is accomplished by obtaining the WLD histogram feature of each sub-image.

  25. Face Recognition Method C. Decision fusion • To improve the performance of the recognition scheme, all recognition results of the sub-images are dealt with by decision fusion through voting as:

  26. A C B Face Recognition Method

  27. 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

  28. Experiments A. Experiments on effect of the different parameters D=TxN

  29. Experiments A. Experiments on effect of the different parameters Divided into X*Y sub-regions

  30. Experiments B. The comparison of WLD,LBP and LTP

  31. Experiments C. Comparison with different methods

  32. 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

  33. 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.

  34. Thank you!

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