【摘要】 提出了一种基于多通道图判别投影HAAR特征的多视角人脸检测算法。该算法首先将人脸图像提取多通道图,降低图像中的光照和噪声影响;其次基于正负训练样本集利用线性判别投影学习增强HAAR特征,提高特征判别能力;然后计算训练样本的增强HAAR特征在多通道图中的响应,并利用非对称GentleBoost算法进行特征选择生成一组弱分类器;最后利用线性非对称分类器重新调整强分类器的权重和阈值。该方法不仅提高了特征的判别能力,而且实现了非平衡正负样本空间的合理划分。实验结果表明:该方法与当前经典方法相比具有更快的检测速度和更高的检测精度。更多还原

【Abstract】 A multi view face detection algorithm based on multi-channel map discriminant projection HAAR feature is proposed.Firstly,the multi-channel map is extracted from the face image by the algorithm,which can reduce the influence of illumination and noise in the image.Secondly,based on the positive and negative training samples,the enhanced HAAR feature is learned by the linear discriminant projection,which can improve the distinguishing ability of the feature.Then the response in multi-channel of the augmented HAAR feature in the training sample is calculated,and the non symmetric GentleBoost algorithm is used to generate a set of weak classifiers.Finally,the weight and threshold of the strong classifier are adjusted by the linear non symmetric classifier.This method not only improves the distinguishing ability of the feature,but also realizes the reasonable division of the non balanced positive and negative sample space.Experimental results show that the proposed method has a faster detection speed and the higher detection accuracy compared with the classical methods.更多还原