聚类与跟踪相结合的人脸数据集生成方法研究

为提高人脸数据集生成的准确率和效率,提出了一种将人脸跟踪与人脸聚类相结合的人脸数据集生成方法.首先,对KCF算法进行改进,并将改进的KCF算法用于人脸跟踪,得到不同时间片段的人脸图像集; 其次,从每个人脸图像集中通过人脸图像优选算法选出高质量的人脸图像; 再次,将优选出来的人脸图像进行人脸聚类,以完成视频中每个人的人脸数据集的生成; 最后,通过实验对比基于人脸跟踪、基于人脸聚类和基于本文方法的人脸数据集生成效果.实验结果表明,本文方法与基于人脸跟踪的人脸数据集生成方法相比,纯度提升约15%; 与基于人脸聚类的人脸数据集生成方法相比,效率提升约50%.

Research on face data set generation method based on combination of clustering and tracking

To improve the accuracy and efficiency of face data set generation, we propose a method of combining face tracking and face clustering to generate face data sets. Firstly, the KCF algorithm is improved and used for face tracking to get face image sets with different time segments. Secondly, a high-quality face image is selected from each face image set by face image optimization algorithm. Thirdly, face clustering is carried out on all optimized face images to generate face data set for each person in the video. Finally, three different face data sets generation experiments of the method based on face tracking, the method based on face clustering and the method proposed in this paper are performed. The experimental results show, comparing with the method of face data sets generation based on face tracking algorithm, the method proposed in this paper has improved about 15% in purity, comparing with the method of face data sets generation based on face clustering algorithm, the method proposed in this paper has improved about 50% in time efficiency.