摘要
人眼检测是计算机人脸识别的重要部分。复杂环境下人眼定位容易受到光照以及人不同姿态的影响。为了解决复杂环境下的人眼定位问题,采用基于支持向量机的人眼检测算法,首先对复杂环境下采集的不同人的人眼样本进行灰度化均衡以及小波变换,将变换结果表示成向量形式,运用序贯最小优化算法进行训练,得到一组支持向量,然后遍历待检测人脸图利用支持向量所构成的分类器进行人眼初检,最后根据先验知识完成信息融合,最终标定人眼。实验结果表明,该算法对各种复杂环境下的含人眼图像有普遍的适应性和有效性。
Eye detection is very important in face recognition .Eye detection on complicated environment is easily to affect by illumination and the posture of people.For solve the eye detection problem on complicated environment, in this paper a method of eye detection based on SVM is put forward.First,using gradation-rization balance and minor wave varies on the eye samples, and taking the result to the form of vector.This algorithm uses SMO method training the eye samples,which come from different people in complex background,and gets SV result.Then uses SVM for image detection,finally,it completes the windows merge.The experimental result indicates that,this algorithm has the common adaptability and validity to each kind of complex environment under including the human eye image.
出处
《计算机工程与应用》
CSCD
北大核心
2008年第24期188-190,共3页
Computer Engineering and Applications
关键词
人眼检测
支持向量机
序贯最小优化
eye-detection
Support Vector Machine（SVM）
Sequential Minimal Optimization（SMO）
