基于上下文约束的人脸聚类算法

Face Clustering Algorithm Based on Context Constraints

人脸聚类将属于同一身份的人脸图片自动分到同一组,可用于人脸标注和图像管理等领域。传统的聚类算法的准确率很高,其召回率往往较低。为了解决这个问题,文中提出了一种加入三角约束关系和上下文约束的聚类算法。该聚类算法基于条件随机场模型,结合三角约束关系,同时考虑图像中常见的上下文约束,分别在聚类迭代过程和初步聚类之后添加最大相似度约束和共同出现约束,并对簇类进行整合。实验结果显示,结合上下文约束的条件随机场模型可以快速进行人脸聚类,同时还能保证在较高准确率的基础上有较高的召回率,从而提高整体的聚类效果。

Face clustering which aims to automatically divide face images of the same identity into the same cluster,can be applied in a wide range of applications such as face annotation,image management,etc.The traditional face clustering algorithms can achieve good precision,but low recall.To handle this issue,this paper proposed a novel clustering algorithm with triangular constraints and context constraints.The proposed algorithm based on conditional random field model takes triangular constraints as well as common context constraints into accountin images.During the clustering iteration and after preliminary clustering,maximum similarity and people co-occurrence constraints are considered to merge the initial clusters.Experimental results reveal that the proposed face clustering algorithm can group faces efficiently,and improve recall with the high precision,and accordingly enhance the overall clustering performance.