基于知识和独立成分的人脸检测

为了提高人脸检测速度及鲁棒性,提出了一种基于知识和独立成分分析(ICA)相结合的人脸检测算法。通过对人脸简化模型的分析,扩充了原有的粗检测规则;为了进一步加快检测速度,采用了先利用知识后利用独立成分分析的两级检测步骤,且在粗检测中采用了几何广义投影法,取得了良好的效果,同时利用最大类间方差法实现了阈值的自适应选择。实验结果表明了该算法的有效性和正确性。

Face Detection Based on Knowledge and Independent Component

In order to improve the speed and robustness of detecting human faces in grayscale scene image, an approach that combines independent component analysis (ICA) with knowledge-based is proposed for face detection. A new detection rule is presented by analyzing face predigest models usually used in painting face. Moreover, in order to further reduce the time of face detection, the hierarchical detection processes and extended projection are used in the process of rough face detection. In addition, adaptive threshold selection is realized by maximizing between-class variance. Experimental results show that the new algorithm is feasible and efficient.