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首页> 外文期刊>Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on >Face Feature Weighted Fusion Based on Fuzzy Membership Degree for Video Face Recognition
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Face Feature Weighted Fusion Based on Fuzzy Membership Degree for Video Face Recognition

机译：基于模糊隶属度的人脸特征加权融合视频人脸识别

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摘要

This paper proposes a new video face recognition (FR) method that is designed for significantly improving FR via adaptive fusion of multiple face features (belonging to the same subject) acquired from a face sequence of video frames. In this paper, we derive an upper bound for recognition error arising from the proposed weighted feature fusion to justify theoretically its effectiveness for recognition from videos. In addition, in order to compute the optimal weights of face features to be fused, we develop a novel weight determination solution based on fuzzy membership function and quality measurement for face images. Using four public video databases, the effectiveness of the proposed method has been successfully evaluated under the conditions that are similar to those in real-world video FR applications. Furthermore, our method is simple and straightforward to implement.
机译：本文提出了一种新的视频人脸识别（FR）方法，该方法旨在通过自适应融合从视频帧的人脸序列中获取的多个人脸特征（属于同一主题）来显着提高FR。在本文中，我们推导了由提出的加权特征融合引起的识别误差的上限，以从理论上证明其对视频识别的有效性。此外，为了计算要融合的人脸特征的最佳权重，我们开发了一种基于模糊隶属函数和人脸图像质量测量的新颖权重确定解决方案。使用四个公共视频数据库，已在与实际视频FR应用程序相似的条件下成功评估了该方法的有效性。此外，我们的方法简单易行。

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