| 摘要: |
| 针对实际生活中普遍存在的多视角、多人脸检测问题，提出了一种新的分类器训练方法及人脸检测的解决方案；首先采用NPD差分特征对人脸特征进行描述，NPD特征通过判断两个像素值间相对差异对人脸进行描述，其特征值可从二维表直接获取，能大大节省训练时间；同时提出了一种深度二叉特征树结构来训练分类器，可有效结合特征间的关联性，将训练得到分类器与肤色算法相结合来提高检测速度；通过在CMU人脸数据库上对所提出算法进行验证，仿真结果证明在多人脸、多视角检测背景环境下，该算法较AdaBoost算法在检测率提高了8.7%，误检率降低了4.1%，检测速度提高了27.7%。 |
| 关键词: NPD特征 深度二叉特征树 肤色 Adaboost算法 |
| DOI： |
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| A Human Face Detection Algorithm Based on Skin and Depth of a Feature Binary Tree |
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YIN Yuan1,2,3, HE Qiang1, WEN Kai1,2
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| Abstract: |
| In this paper, a new classifier training method and human face detection solution are proposed for multi face and multi view face detection problems which are prevalent in real life. Firstly, NPD (Normalized Pixel Difference) feature is used to describe the facial feature, the NPD feature describes the human face by judging the relative difference between the two pixel values, its eigenvalues can be obtained directly from the two dimensional table, which can greatly save the training time. At the same time, a kind of depth binary function tree structure is proposed to train the classifier, which can effectively combine the correlation between the features and combine the training with the skin color algorithm to improve the detection speed. Through the test of the proposed algorithm in this paper in CMU human face database, simulation results indicate that the algorithm improves the detection rate by 8.7%, error detection rate reduced 4.1%, detection speed improved 27.7% under multi face and multi view background. |
| Key words: NPD feature depth of binary tree feature skin color Adaboost algorithm |
