Masked-Piper: masking personal identities in visual recordings while preserving multimodal information
- In this increasingly data-rich world, visual recordings of human behavior are often unable to be shared due to concerns about privacy.
Consequently, data sharing in fields such as behavioral science, multimodal communication, and human movement research is often limited.
In addition, in legal and other non-scientific contexts, privacy-related concerns may preclude the sharing of video recordings and thus remove the rich multimodal context that humans recruit to communicate.
Minimizing the risk of identity exposure while preserving critical behavioral information would maximize utility of public resources (e.g., research grants) and time invested in audio-visual research.
Here we present an open-source computer vision tool that masks the identities of humans while maintaining rich information about communicative body movements. Furthermore, this masking tool can be easily applied to many videos, leveraging computational tools to augment the reproducibility and accessibility of behavioral research.
The tool is designed forIn this increasingly data-rich world, visual recordings of human behavior are often unable to be shared due to concerns about privacy.
Consequently, data sharing in fields such as behavioral science, multimodal communication, and human movement research is often limited.
In addition, in legal and other non-scientific contexts, privacy-related concerns may preclude the sharing of video recordings and thus remove the rich multimodal context that humans recruit to communicate.
Minimizing the risk of identity exposure while preserving critical behavioral information would maximize utility of public resources (e.g., research grants) and time invested in audio-visual research.
Here we present an open-source computer vision tool that masks the identities of humans while maintaining rich information about communicative body movements. Furthermore, this masking tool can be easily applied to many videos, leveraging computational tools to augment the reproducibility and accessibility of behavioral research.
The tool is designed for researchers and practitioners engaged in kinematic and affective research. Application areas include teaching/education, communication and human movement research, CCTV, and legal contexts.…
| Author details: | Babajide OwoyeleORCiD, James Trujillo, Gerard de MeloORCiDGND, Wim PouwORCiD |
|---|---|
| DOI: | https://doi.org/10.1016/j.softx.2022.101236 |
| ISSN: | 2352-7110 |
| Title of parent work (English): | SoftwareX |
| Publisher: | Elsevier |
| Place of publishing: | Amsterdam |
| Publication type: | Article |
| Language: | English |
| Date of first publication: | 2022/11/03 |
| Publication year: | 2022 |
| Release date: | 2024/08/26 |
| Tag: | data privacy; kinematic research; masking; multimodal communication; open science; research reproducibility |
| Volume: | 20 |
| Article number: | 101236 |
| Number of pages: | 4 |
| Funding institution: | VENI - Dutch Research Council (NWO) [VI.Veni.201G.047]; Dutch Research; Council (NWO) |
| Organizational units: | An-Institute / Hasso-Plattner-Institut für Digital Engineering gGmbH |
| DDC classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke |
| Peer review: | Referiert |
| Publishing method: | Open Access / Gold Open-Access |
| DOAJ gelistet | |
| License (German): |  CC-BY - Namensnennung 4.0 International |
