基于SVM实现人眼注视与否的探知  

Gazing Detection of Human Eyes Based on SVM

作　　者：李素梅[1] 张延[1] 常胜江[1] 申金媛[1] 李宜宾[1] 王立[1] 

机构地区：[1]南开大学现代光学研究所教育部光电信息技术重点实验室,天津300071

出　　处：《光电子．激光》2004年第10期1229-1233,共5页Journal of Optoelectronics·Laser

基　　金：国家自然科学基金资助项目(60277022);天津市自然科学基金重点资助项目(023800811);教育部博士点基金资助项目(20030055022)

摘　　要：采用基于统计学习理论的支持向量机(SVM,supportvectormachine)方法对人眼注视与否进行探知。根据结构风险最小化(SRM,structuralriskminimization)准则,在最小化已知样本点误差的同时,尽量缩小模型预测误差的上界,改善了模型的泛化能力。实验结果显示,在训练样本数有限的情况下,学习后模型对测试样本的正确识别率达到100%,比此前采用其它方法所获得的识别结果识别率更高,训练及识别过程速度更快,基本上能够满足实时性要求,也更接近人类视觉对注视与否的探知的特点。A method for gazing detection of human eyes is proposed by using Support Vector Machine (SVM) based theoretically on statistic learning theory (SLT). According to the criteria of structural risk minimization of SVM, the errors between sample-data and model-data are minimized and the upper bound of predicting error of the model is also decreased simultaneously so that the ability of generalization of the model is much improved. The simulation results show that when limited training samples are used, the obtained correct recognition rate of the testing samples can be as high as 100% which is much better than some previous results by other methods. The higher processing speed enables the system distinguishes the gazing direction in real-time, as well as to better approach to the characteristics of gazing detection of human vision.

关 键 词：SVM 识别率 人类视觉 结构风险最小化 统计学习理论 泛化能力 支持向量机 上界 样本点 已知 

分 类 号：TN912[电子电信—通信与信息系统] TP391[电子电信—信息与通信工程]