Title | ||
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Recognizing irrelevant faces in short-form videos based on feature fusion and active learning |
Abstract | ||
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In recent years, short-form videos spread rapidly around the world and became a popular way of entertainment for people to share their daily lives. However, many videos record behaviors of other people without their awareness and are uploaded onto the short-form video platforms. Such behavior severely invades personal privacy and can even bring risks of personal information leakage. At present, few studies focus on detecting privacy violations in short-form videos. Meanwhile, due to the difficulty in transferring existing models to the scenario of short-form videos and the lack of reliable datasets, it is very challenging to recognize irrelevant faces in short-form videos. To deal with this problem, we constructed and published an irrelevant faces dataset (IF-Dataset) with 43,965 irrelevant face images and 89,924 relevant face images based on the videos collected from Douyin (the Chinese version of TikTok). In addition, we constructed a framework that implemented our proposed deep learning model Multi-features Multi-head Fusion Network (MMFNet) to recognize irrelevant faces from short-form videos. The experimental results show that the F1 score of the MMFNet can reach 87.03%. We also proposed a novel loss function as well as an active learning system to improve the generalization ability of models, which can reach the Relative Error Reduction (RER) up to 29.58%. Our work provides both theoretical and practical support for face protection in short-form videos. |
Year | DOI | Venue |
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2022 | 10.1016/j.neucom.2022.06.064 | Neurocomputing |
Keywords | DocType | Volume |
Face protection,Feature fusion,Loss function,Domain adaptation | Journal | 501 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Mingcheng Zhu | 1 | 0 | 0.34 |
Rongchuan Zhang | 2 | 0 | 0.34 |
Haizhou Wang | 3 | 0 | 1.01 |