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基于Adaboost与支持向量机的人脸特征提取 被引量:9

Facial Feature Selection Based on Adaboost and Support Vector Machine
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摘要 针对单一分类器人脸检测非常耗时的问题,提出了一种由粗到精的融合分类器结构模式加速人脸检测。该系统分为3个阶段:前两个阶段,使用Adaboost级联分类器快速排除大量简单的非人脸图像;最后一个阶段,使用非线性的支持向量机分类器,将已通过前两个阶段检测的复杂图像准确归类为人脸或非人脸。实验结果表明系统性能良好。 Aiming at the problem that training time of face detection using single classifier is extremely long, this paper present a combination of simple-to-complex and three-stage classifiers to speed up a face detection system. In this proposed system, a large number of simple non-face patterns are rejected quickly by two first stage Adaboost cascaded classifiers while the last stage uses a non-linear SVM classifier to robustly classify complex patterns that have past two first stage as either faces or non-faces. Experimentalresults show that our system can achieve comparable results to state of the art face detection systems.
作者 张威 魏冬生
出处 《微电子学与计算机》 CSCD 北大核心 2007年第5期69-72,共4页 Microelectronics & Computer
关键词 人脸检测 模式识别 ADABOOST算法 支持向量机 face detection pattern recognition adaboost algorithm Support Vector Machine(SVM)
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参考文献6

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