Facial feature location is important for face analysis in computer vision. In order to enhance accuracy and efficiency in unconstrained environment with various head poses, background, occlusion, illumination and make-up, we propose a conditional iteration updated random forest approach for precise facial feature location. In order to eliminate occlusion and background noise, positiveegative facial patches are classified from a facial area, firstly; then, initial facial feature positions are detected by the trained initial conditional probabilistic model under the condition of 25 head pose models in three local sub-regions; the multiple leaves of random forests are updated using the on-line learning in an iterative way based on the detected facial feature initial position and facial error deformation, which include patches’ classes, head pose probabilities, face deformation model and face error offset model; finally, a conditional weighted sparse voting method is introduced into the interactive regression model for voting final precise facial feature loca-tions. Experiments on the challenging AFW, LFW, and Pointing'04 datasets demonstrated that the average accuracy of our approach has reached 95% with only 0.15 mean errors for facial feature location in uncon-strained environment.%人脸特征点定位是计算机视觉中研究和分析人脸的关键.为了提高在非约束环境中(大姿态变化、遮挡、复r杂背景等)人脸特征点定位的准确性和鲁棒性提出一种基于条件迭代更新随机森林的非约束人脸特征点定位方法.r首先为了克服遮挡和背景噪声的影响对人脸子区域进行分类提取人脸正子区域;然后在人脸正子区域上估计r头部姿态根据估计的头部姿态和人脸局部子区域学习特征点的初始化条件概率模型定位人脸特征点的初始位置;r再依据特征点的初始位置建立人脸误差模型利用误差模型在线学习并多次迭代更新随机森林的叶子节点生成新r的复合叶子概率模型包括人脸子块类别、头部姿态、人脸形变模型、误差偏移模型;最后引入条件权重稀疏投票r对复合叶子概率模型进行回归定位人脸特征点的精确位置.在AFWLFW和Pointing'04这3个具有挑战性的公共r人脸数据库上进行实验的结果表明该方法在非约束人脸特征点定位中的平均误差值为0.15时定位准确率超过95%.
展开▼
