学习笔记:LAB Feature with Feature-centric Cascade for Fast and Accurate Face Detection
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该笔记只是用于知识总结,且本人第一次写博客。请多担待。
LAB特征是中科院计算所山世光研究员团队提出来的,主要用于人脸检测。
LAB的概念:全称是Locally Assembled Binary ,主要是将Haar特征按照LBP特征的方式进行提取。也就是黑色矩形的像素减去白色矩形的像素的结果如果大于0就令该特征为1,否则为0。公式如下:
Haar特征计算方法:
σ 是候选窗口x的方差。
组合的方式:
此例子中,3个二进制Haar特征组合成了一个Assembled Binary Haar (ABH) 特征。
LAB特征检测分为两个过程。先在以“以特征为中心”的检测方式中提取出特征值图像,排除掉简单的非人脸,然后再在特征值图像上采用以“窗口为中心”的检测方式进一步排除点那些困难的非人脸。如图所以:
c是分类函数,x是样本窗口,h是弱分类器函数,li是特征i的特征计算函数,N是总特征数。
LAB特征检测方法在seetaface人脸识别引擎中的第一级被采用了。LAB算法具有一定的光照变换的鲁棒性,旋转不变性。而且识别速度比Haar特征检测法快20倍。
如上图所示,LAB特征有9个矩形,每个矩形设计为3*3的尺寸。中间的黑色区域是9个矩形共享的。
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