一种基于预测的实时人脸特征点定位跟踪算法  

Real-time facial feature point location and tracking algorithm based on prediction mechanism

作　　者：翁政魁 王彬[1] 王坤[2] 刘辉[1] 

机构地区：[1]昆明理工大学信息工程与自动化学院,昆明650500 [2]华中科技大学自动化学院,武汉430074

出　　处：《计算机工程与应用》2015年第12期198-202,共5页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61263017);云南省自然科学基金(No.2011FZ060);昆明理工大学人才培养基金资助项目(No.KKSY201303120);国家大学生创新创业训练计划项目(No.201210674011);国家留学基金管理委员会资助项目(留金发[2011]52014号)

摘　　要：当前的人脸特征点定位跟踪方法因其计算量大,实时特性欠佳。给出了一种基于改进Viola-Jones算法和Kalman滤波器预测机制的定位及跟踪算法。该算法通过使用改进的Viola-Jones算法对本次人脸特征点进行定位,同时使用Kalman滤波算法对特征点下次出现位置进行预测,缩小了下一帧特征点定位过程中特征点的搜索范围,因而缩短了定位搜索时间。实验结果表明该方法在保证定位准确性和鲁棒性的同时明显增强了算法的实时性。The current research of location and tracking methods for facial feature point are poor in real-time performance because they are large in computing capacity. In this paper, an improved method based on Viola-Jones algorithm with Kalman filter prediction mechanism is presented. The current facial feature point is located by using Viola-Jones algorithm and the scope where the next feature point will appear is predicted by Kalman filter algorithm. As a result, the scope of the feature point in next frame is reduced and the locating time is shortened. Experiments show with this method the real-time performance of facial feature point location and tracking algorithm can be improved apparently while ensuring the accuracy and robustness.

关 键 词：人脸特征点识别 特征点跟踪 预测机制 KALMAN滤波器 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]