摘要
针对AD AdaBoost算法在样本训练过程中的退化现象,提出了一种基于改进的AD AdaBoost算法的人眼检测方法,通过释放正确分类负样本的权值并进行归一化处理,缓冲分类困难的样本上权值的扩张.实验结果表明此方法在保持较好实时性的同时,能够提高检测的准确率.
Eye detection is an important step in eye tracking and eye state recognition.An improved AD AdaBoost algorithm for eye detection is proposed to slow the degradation in training step.Weight on negative samples which are classified correctly is released then the other samples＇ weight is normalized to slow the expansion of weight on difficult samples.The experiment results show that the approach proposed is real time and has a higher detection accuracy.
出处
《西南师范大学学报（自然科学版）》
CAS
CSCD
北大核心
2010年第3期247-249,共3页
Journal of Southwest China Normal University(Natural Science Edition)
