基于多尺度图像局部结构分解的人脸特征提取方法

doi:10.3969/j.issn.1006-2475.2015.03.011

计算机与现代化

基于多尺度图像局部结构分解的人脸特征提取方法

南京理工大学计算机科学与技术学院,江苏南京210094

收稿日期:2014-12-01 出版日期:2015-03-23 发布日期:2015-03-26
作者简介:冯翔(1989-),男,安徽安庆人,南京理工大学计算机科学与技术学院硕士研究生,研究方向:图像处理,人脸识别; 杨健(1973-),男,江苏南京人,教授,博士,研究方向:人脸识别,计算机视觉; 钱建军(1983-),男,江苏南京人,博士,研究方向:人脸识别,计算机视觉。

Face Feature Extraction and Recognition Method for Multi-scale Local Structure-based Image Decomposition

School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China

Received:2014-12-01 Online:2015-03-23 Published:2015-03-26

摘要/Abstract

摘要： 为了有效提取人脸图像的全局和局部特征以提高人脸识别的性能，提出一种基于多尺度图像局部结构分解的人脸特征提取方法。该方法首先通过多尺度分析构建人脸图像金字塔，然后对于金字塔中每一层的图像应用脊回归度量图像局部窗口内中心宏像素与其近邻宏像素之间的结构关系从而刻画出图像的局部结构信息，再根据得到的局部结构信息将图像分解为若干个子图像，最后将这些子图像均匀下采样和归一化后连接在一起形成一个特征向量。实验结果表明，与Gabor、LBP和IDLS等方法相比，该方法具有更好的识别性能。


关键词: 多尺度, 图像金字塔, 图像分解, 局部结构特征, 人脸识别

Abstract: In order to effectively extract the global and local features to improve the performance of face recognition, this paper presents a robust yet simple feature extraction method, called multi-scale image decomposition based on local structure. In the algorithm, the face image pyramid is first constructed through a multi-scale analysis. Then the local structural information by describing the relationship between the central macro-pixel and its neighbors for each level of the image pyramid is captured. In this way, one image is actually decomposed into a series of sub-images. Finally, all the structure images, after being down-sampled, are concatenated in one super-vector. Experimental results show that the proposed method is superior to some traditional methods such as Gabor, LBP and IDLS.

Key words: multi-scale, image pyramid, image decomposition, local structure feature, face recognition

中图分类号:

TP391

冯翔,杨健,钱建军. 基于多尺度图像局部结构分解的人脸特征提取方法[J]. 计算机与现代化, doi: 10.3969/j.issn.1006-2475.2015.03.011.

FENG Xiang, YANG Jian, QIAN Jian-jun. Face Feature Extraction and Recognition Method for Multi-scale Local Structure-based Image Decomposition[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2015.03.011.

参考文献

［1]孔令钊,唐文静. 基于PCA的人脸识别系统的研究与实现［J]. 计算机仿真, 2012,29(6):27-29.
［2]Bartlett M S, Movellan J R, Sejnowski T J. Face recognition by independent component analysis［J]. IEEE Transactions on Neural Networks, 2002,13(6):1450-1464.
［3]张健,肖迪. 基于多尺度自适应LDA的人脸识别方法［J]. 计算机工程与设计, 2012,33(1):332-335.
［4]Liu Chengjun, Weehsler H. Independent componet analysis of Gabor features for face recognition［J]. IEEE Transaction on Neural Networks, 2003,14(4):919-928.
［5]许凤娇,王国胤. 年龄变化条件下采用Gabor金字塔的人脸描述与识别［J]. 计算机应用, 2013,33(3):695-699.
［6]Ahonen T, Hadid A, Pietikainen M. Face recognition with local binary patterns［C]// Proceedings of the 8th European Conference on Computer Vision. Prague, Czech, 2004,3021:469-481.
［7]王玮,黄菲菲,李见为,等. 采用LBP金字塔的人脸描述与识别［J]. 计算机辅助设计与图形学学报, 2009,21(1):94-100.
［8]Tan Xiaoyang, Triggs B. Enhanced local texture feature sets for face recognition under difficult lighting conditions［J]. IEEE Transction on Image Processing, 2010,19(6):1635-1650.
［9]Zhang Baochang, Gao Yongsheng, Zhao Sanqiang, et al. Local derivative pattern versus local binary pattern: Face recognition with high-order local pattern descriptor［J]. IEEE Transaction on Image Processing, 2010,19(2):533-544.
［10]Ahonen T, Hadid A, Pietikainen M. Face description with local binary patterns: Application to face recognition［J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006,28(12):2037-2041.
［11]Zhang Wenchao, Shan Shiguang, Gao Wen, et al. Local gabor binary pattern histogram sequence(LGBPHS): A novel non-statistical model for face representation and recognition［C]// Proceedings of the 10th IEEE International Conference on Computer Vision. 2005,1:786-791.
［12]Lanitis A, Taylor C J, Cootes T F. Automatic face indentification system using flexible appearance models［J]. Image and Vision Computing, 1995，13(5):393-401.
［13]Wiskott L, Fellous J M, Kruger N, et al. Face recognition by elastic bunch graph matching［J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997,19(7):775-779.
［14]Qian Jianjun, Yang Jian, Xu Yong. Local structure-based image decomposition for feature extraction with applications to face recognition［J]. IEEE Transtractions on Image Processing, 2013,22(9):3591-3603.
［15]Martinez A, Benavente R. The AR Face Database［R]. Technical Report 24, Computer Vision Center, Purdue University, 1998.
［16]Lee K C, Ho J, Kriegman D J. Acquiring linear subspaces for face recognition under variable lighting［J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005,27(5):684-698.
［17]Sim T, Baker S, Bsat M. The CMU pose, illumination, and expression database［J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003,25(12):1615-1618.

[1]	王鑫, 肖韬睿. 基于生成对抗网络的人脸识别对抗攻击[J]. 计算机与现代化, 2023, 0(10): 115-120.
[2]	李国鑫, 曲寒冰, 朱成博, 王鑫轩, 胡嘉宝. 基于联合网络的域适应行人重识别算法[J]. 计算机与现代化, 2023, 0(06): 48-55.
[3]	朱理清, 李祥, . 改进YOLOv5算法的遥感图像车辆检测[J]. 计算机与现代化, 2023, 0(05): 117-121.
[4]	尹相臣, 陈思龙, 李振凯, 张文进, 李桂青. 多尺度密集感受域的GAN图像去雾算法[J]. 计算机与现代化, 2023, 0(03): 11-15.
[5]	盛江岸, 陈淑荣. 融合双重注意力机制的戴口罩人脸识别方法[J]. 计算机与现代化, 2023, 0(02): 72-77.
[6]	梁科晋, 张海军, 刘雅情, 张昱, 王月阳. 混合多尺度卷积结合双层LSTM语音情感识别[J]. 计算机与现代化, 2023, 0(01): 63-68.
[7]	蒋思清, 陈潇俊, 高豪俊, 何佳晋, 吴健, . 基于对抗域适应的心电信号深度学习分类算法[J]. 计算机与现代化, 2023, 0(01): 81-87.
[8]	任朝宇, 赵冬娥, 张斌, 杨学峰, 褚文博. 基于多尺度CLG光流法的多目标检测方法[J]. 计算机与现代化, 2022, 0(04): 33-37.
[9]	梁正友, 耿经邦, 孙宇. 基于改进残差网络的交通标志识别算法[J]. 计算机与现代化, 2022, 0(04): 52-57.
[10]	陈中杰, 田建辉, 胡光初, 丁锋, 郭钊, 韩兴本. 基于特征识别的多尺度构件网格自动生成算法[J]. 计算机与现代化, 2022, 0(03): 91-97.
[11]	陈勋豪, 杨莹, 黄俊茹, 孙玉宝. 基于多尺度融合网络的视频快照压缩感知重建[J]. 计算机与现代化, 2021, 0(12): 58-64.
[12]	汪帆, 魏宪, 郭杰龙, 梁培栋. 基于多通道分离整合的多尺度单幅图像去雨算法[J]. 计算机与现代化, 2021, 0(12): 72-78.
[13]	程凯强, 张旭, 寇旭鹏. 基于SSD的小目标检测改进算法[J]. 计算机与现代化, 2021, 0(07): 77-82.
[14]	徐睿, 李志华, 韩灿灿. 基于特征融合和改进卷积神经网络的环境音识别[J]. 计算机与现代化, 2021, 0(02): 62-67.
[15]	吴水明, 朱燕, 王芳, 景栋盛. 一种基于Cascade R-CNN的电子器件容器质检方法[J]. 计算机与现代化, 2020, 0(11): 33-38.

基于多尺度图像局部结构分解的人脸特征提取方法

Face Feature Extraction and Recognition Method for Multi-scale Local Structure-based Image Decomposition

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价