吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (2): 606-612.doi: 10.13229/j.cnki.jdxbgxb201502040

• 论文 • 上一篇    下一篇

基于思维进化算法的人脸特征点跟踪

李根，李文辉   

  1. 吉林大学 计算机科学与技术学院，长春 130012
  • 收稿日期:2013-11-22 出版日期:2015-04-01 发布日期:2015-04-01
  • 通讯作者: 李文辉(1961)，男，教授，博士生导师.研究方向:计算机图形学，数字图像处理，计算机视觉.E-mail:liwh@jlu.edu.cn
  • 作者简介:李根(1982)，男，博士研究生.研究方向:计算机视觉，数字图像处理.E-mail:genlee@sina.com
  • 基金资助:

    国家自然科学基金项目(60873147)；吉林省科技发展计划重点项目(20120305).

Facial feature tracking based on mind evolutionary algorithm

LI Gen,LI Wen-hui   

  1. College of Computer Science and Technology, Jilin University, Changchun 130012, China
  • Received:2013-11-22 Online:2015-04-01 Published:2015-04-01

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摘要：

提出了一种基于思维进化算法的人脸特征点跟踪的方法。通过提取人脸跟踪区域的尺度不变特征变换获得整体特征模版，并确定跟踪特征点，由整体特征模版的空间分布关系限定跟踪特征点的存在区域。应用思维进化算法的趋同过程和异化过程求得整体特征模版在目标帧的最优覆盖解，进一步提高跟踪特征点的限定区域的准确性，提高搜索速度和精度。引入原始特征和替代特征，使算法在复杂情况下仍能保持稳定跟踪和较快的跟踪速率。经试验，新方法能够在复杂情况下稳定跟踪95%的特征点，并保持25帧/s的跟踪速率。

关键词: 计算机应用, 思维进化算法, SIFT特征, 特征点跟踪, 特征点约束

Abstract:

A new facial feature point tracking method based on Mind Evolutionary Algorithm (MEA) was presented. The integrated feature model was formed by Scale Invariant Feature Transform (SIFT) feature in tracking region. Then the feature points were determined. The existing region of the feature points were restricted by the space distribution of the integrated feature model. The MEA was used to compute the best-case coverage solution by similartaxis procedure and dissimilation procedure in target frames. It can more precisely determine the restriction region of tracking feature points, and improve tracking precision and efficiency. The original feature and replaced feature were introduced to make the tracking steadier and faster in complicated condition. The experiment results indicate that the new method can stably track 95% feature points in complicated condition, while the tracking speed can be kept withing 25 frames per second.

Key words: computer application, mind evolutionary algorithm (MEA), SIFT feature, feature point tracking, feature point restriction

中图分类号: 

  • TP391
[1] Cootes T, Walker K, Taylor C. View-based active appearance models[C]∥Proceedings of 4th IEEE International Conference on Automatic Face and Gesture Recognition. Grenoble, French: IEEE, 2000: 227-232.
[2] 金城,卜佳俊,陈华,等. 自底向上的人脸特征点定位[J].浙江大学学报:工学版,2008, 42(5):794-799.
Jin Cheng, Bu Jia-jun, Chen Hua, et al. Human face detection and feature tracking in a bottom-up way[J]. Journal of Zhejiang University (Engineering Science), 2008, 42(5):794-799.
[3] Hou X, Li S, Zhan G H, et al. Direct appearance models[C]∥Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01). Hawaii: IEEE, 2001: 828-833.
[4] 段鸿,程义民,王以孝,等. 基于Kanade-Lucas-Tomasi算法的人脸特征点跟踪方法[J].计算机辅助设计与图形学学报,2004,16(3):279-283.
Duan Hong, Cheng Yi-min, Wang Yi-xiao, et al. Tracking facial feature points using Kanade-Lucas-Tomasi approach[J]. Journal of Computer- aided Design & Computer Graphics, 2004, 16(3):279-283.
[5] 陈志敏,薄煜明,吴盘龙,等. 基于自适应粒子群优化的新型粒子滤波在目标跟踪中的应用[J]. 控制与决策,2013,28(2):193-200.
Chen Zhi-min, Bo Yu-ming, Wu Pan-long, et al. Novel particle filter algorithm based on adaptive particle swarm optimization and its application to radar target tracking[J]. Control and Decision, 2013, 28(2): 193-200.
[6] 蔺海峰,马宇峰,宋涛. 基于SIFT特征目标跟踪算法研究[J].自动化学报,2010,36(8):1204-1208.
Lin Hai-feng, Ma Yu-feng, Song Tao. Research on object tracking algorithm based on SIFT[J]. Acta Automatic Sinica, 2010, 36(8): 1204-1208.
[7] 战江涛,刘强,柴春雷. 基于三维模型与Gabor小波的人脸特征点跟踪方法[J].浙江大学学报:工学版,2011,45(1):30-36.
Zhan Jiang-tao, Liu Qiang, Chai Chun-lei. Facial feature tracking using three-dimensional model and Gabor wavelet[J]. Journal of Zhejiang University (Engineering Science), 2011, 45(1): 30-36.
[8] Tong Yan, Wang Yang, Zhu Zhi-wei, et al. Robust facial feature tracking under varying face pose and facial expression[J]. Pattern Recognition, 2007, 40(11): 3195-3208.
[9] 李根,李文辉. 基于尺度不变特征变换的平面旋转人脸检测[J].吉林大学学报:工学版,2013,43(1):186-191.
Li Gen, Li Wen-hui. Face detection under rotation in image plane based on scale invariant feature transform[J]. Journal of Jilin University (Engineering and Technology Edition), 2013, 43(1): 186-191.
[10] Brown M, Lowe D G. Invariant features from interest point groups[C]∥Proceedings of British Machine Vision Conference Cardiff,Wales,2002:656-665.
[11] David G L. Distinctive image feature from scale-invariant interest points[J]. International Journal of Computer Vision,2004, 60(2):91-110.
[12] 丁莹,李文辉,范静涛,等. 基于Choquet模糊积分的运动目标检测算法[J]. 电子学报,2010, 38(2):263-268.
Ding Ying,Li Wen-hui,Fan Jing-tao,et al. A moving object detection algorithm base on choquet integrate[J]. Chinese Journal of Electronics, 2010, 38(2): 263-268.
[13] 王川龙,孙承意. 基于思维进化的MEBML算法的收敛性研究[J].计算机研究与发展,2000,37(7):838-842.
Wang Chuan-long, Sun Cheng-yi. A study of mind-evolution-based machine learning[J]. Journal of Computer Research & Development, 2000,37(7):838-842.
[14] 王芳,谢克明,刘建霞.基于群体智能的思维进化算法设计[J].控制与决策,2010,25(1):145-148.
Wang Fang, Xie Ke-ming, Liu Jian-xia. Swarm intelligence based MEA design[J]. Control and Decision,2010,25(1):145-148.
[15] Romdhani S, Gong S, Psarrouj A. Multi-view nonlinear active shape model using kernel PCA[C]∥Proceedings of BMVC. Nottingham: IEEE, 1999: 483-492.
[16] 李文辉,姜园媛,王莹,等. 一种基于重采样双向2DLDA融合的人脸识别算法[J].电子学报,2011,39(11):2526-2533.
Li Wen-hui,Jiang Yuan-yuan,Wang Ying, et al. A face recognition algorithm using a fusion method based on resampling bidirectional 2DLDA[J]. Chinese Journal of Electronics, 2011,39(11): 2526-2533.
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