基于代价敏感深度决策树的公交车环境人脸检测

Face detection in bus environment based on cost-sensitive deep quadratic tree

针对公交车环境下的人脸检测具有光照变化、模糊、遮挡、低分辨率和姿势变化等问题,提出了基于代价敏感深度决策树的人脸检测算法。首先,基于归一化的像素差异(NPD)特征构建单个深度二次树(DQT);接着,根据当前决策树的分类结果,利用代价敏感Gentle Adaboost方法对样本权重进行更新,依次训练出多棵深度决策树;最后,将所有决策树通过Soft-Cascade级联得到最终的检测算法。在人脸检测数据集(FDDB)和公交车视频上的实验结果表明,所提算法与现有的深度决策树算法相比,在检测率和检测速度上均有提升。

The problems of face detection in bus environment include ambient illumination changing, image distortion, human body occlusion, abnormal postures and etc. For alleviating these mentioned limitations, a face detection based on cost- sensitive Deep Quadratic Tree （DQT） was proposed. First of all, Normalized Pixel Difference （NPD） feature was utilized to construct and train a single DQT. According to the classification result of the current decision tree, the cost-sensitive Gentle Adaboost method was used to update the sample weight, and a number of deep decision trees were trained. Finally, the classifier was produced by Soft-Cascade method with multiple upgraded deep quadratic trees. The experimental results on Face Detection Data set and Benchmark （FDDB） and bus video show that compared with the existing depth decision tree algorithm, the proposed algorithm has improved the detection rate and detection speed.