不确定性估计结合主动外观模型三维特征提取的人脸识别方法  

Face recognition method based on uncertainty measurement combined with 3D features extraction using active appearance model

作　　者：卜宇[1] 任晓芳[1] 唐学军[1] 孙挺[2] 

机构地区：[1]新疆工程学院计算机工程系,乌鲁木齐830052 [2]西北大学可视化研究所,西安710069

出　　处：《计算机应用》2016年第7期1971-1975,共5页journal of Computer Applications

基　　金：国家973计划前期研究专项(2011CB311802)~~

摘　　要：对于人脸识别分类结果中的可信度问题,提出一种基于不确定性理论的人脸识别方法。首先,为了估计3D特征,使用主动外观模型(AAM)和三角测量处理两幅未知对象的2D图像;然后,估计数据库中每个对象的分数,通过不确定性进一步处理两幅图像;最后,决策过程根据估计的分数和估计的不确定性分类列表,其中分类列表中存储了所有已识别对象及其对应的可信度。实验采用含两个摄像头的立体视觉系统采集各种姿态的人脸图像。与类似的概率预测测量方法相比,所提方法的正确检测率提高10%左右,漏检率至少降低了9%。实验结果表明,所提方法通过构建3D图像特征的不确定性信息和采用合适的统计方法提高了分类结果的准确率。Concerning the credibility problem of the classification results in face recognition, a face recognition method based on the theory of uncertainty was proposed. Firstly, in order to estimate 3D features, Active Appearance Model（ AAM）and triangulation were used to process two 2D images of unknown object. Then, the score of each object in the database was estimated, and two images were further processed through uncertainty. Finally, the decision was made based on the estimated scores and the estimated uncertainty classification list. All identified objects and their corresponding credibilities were stored in the classification list. Stereo vision system with two cameras captures face images of various postures in the experiment.Compared with a similar probability forecasting measurement method, the correct detection rate of the proposed method was increased by 10%, and the false detection rate was reduced by at least 9%. The experimental results show that the classification accuracy is improved by constructing the uncertainty information of 3D image feature and adopting appropriate statistical method.

关 键 词：人脸识别 可信度 不确定性 生物识别算法 分类器 

分 类 号：TP391.413[自动化与计算机技术—计算机应用技术]