Skeleton-Based Typing Style Learning for Person Identification

Lior Gelberg, David Mendlovic, Dan Raviv

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present a novel approach for person identification based on typing-style, using a novel architecture constructed of adaptive non-local spatio-temporal graph convolutional network. Since type style dynamics convey meaningful information that can be useful for person identification, we extract the joints positions and then learn their movements' dynamics. Our non-local approach increases our model's robustness to noisy input data while analyzing joints locations instead of RGB data provides remarkable robustness to alternating environmental conditions, e.g., lighting, noise, etc.. We further present two new datasets for typing style based person identification task and extensive evaluation that displays our model's superior discriminative and generalization abilities, when compared with state-of-the-art skeleton-based models.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages369-378
Number of pages10
ISBN (Electronic)9781665458245
DOIs
StatePublished - 2022
Event2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2022 - Waikoloa, United States
Duration: 4 Jan 20228 Jan 2022

Publication series

NameProceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2022

Conference

Conference2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2022
Country/TerritoryUnited States
CityWaikoloa
Period4/01/228/01/22

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