Face Anthropometry Aware Audio-visual Age Verification
| Type of publication: | Conference paper |
| Citation: | Korshunov_ACMMM_2022 |
| Publication status: | Accepted |
| Booktitle: | ACM Multimedia |
| Year: | 2022 |
| Month: | October |
| Crossref: | Idiap-Internal-RR-22-2022 |
| Abstract: | Protection of minors against destructive content or illegal advertising is an important problem, which is now under increasing societal and legislative pressure. The latest advancements in an automated age verification is a possible solution to this problem. There are however limitations of the current state of the art age verification methods, specifically, the lack of approaches focusing on video-based or even solely audio-based approaches, since the image domain is the one with the majority of publicly available datasets. In this paper, we consider the problem of age verification as a multimodal problem by proposing and evaluating several audio- and image-based models and their combinations. To that end, we annotated a set of publicly available videos with age labels, with a special focus on the children age labels. We also propose a new training strategy based on the adaptive label distribution learning (ALDL), which is driven by facial anthropometry and age-based skin degradation. This adaptive approach demonstrates the best accuracy when evaluated across several test databases. |
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| Projects |
Idiap MARGIN |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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