PB
P. Benschop
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The face and its surrounding context are a strong signal for video analysis in sensitive domains, powering action recognition in forensics and longitudinal emotion analysis in medicine. However, faces are biometric data that privacy regulations such as the GDPR and HIPAA protect, forbidding their storage without protective measures. Pseudonymization solves this problem by replacing each face with a generated one, called a pseudonym. To remain useful, a pseudonymization method must satisfy three requirements: preserving the context around the face, mapping the same subject to the same pseudonym across separate videos, and avoiding any sensitive database that links subjects to their pseudonyms. No existing method satisfies all three. Face swapping preserves context but depends on a vulnerable identity database to stay consistent, while subject- and key-conditioned pseudonym generators remove that database but discard the original frame along with its context. This thesis closes the gap with SKPG-Swap: a hybrid framework in which a lightweight Subject- and Key-conditioned Pseudonym Generator (SKPG) derives a consistent pseudonym from a subject's face and a secret key, combined with a face-swap model which blends that pseudonym back into the original frame. Evaluated against bounding-box rendering strategies built on the same SKPG backbone, SKPG-Swap retains nearly all of the action-recognition accuracy of unmodified videos on UCF101 and outperforms the other pseudonymization methods on RAVDESS emotion recognition. A controlled experiment further shows that assigning a subject a consistent pseudonym identity, rather than an inconsistent one, results in more stable predictions across videos, motivating the consistency requirement.
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The face and its surrounding context are a strong signal for video analysis in sensitive domains, powering action recognition in forensics and longitudinal emotion analysis in medicine. However, faces are biometric data that privacy regulations such as the GDPR and HIPAA protect, forbidding their storage without protective measures. Pseudonymization solves this problem by replacing each face with a generated one, called a pseudonym. To remain useful, a pseudonymization method must satisfy three requirements: preserving the context around the face, mapping the same subject to the same pseudonym across separate videos, and avoiding any sensitive database that links subjects to their pseudonyms. No existing method satisfies all three. Face swapping preserves context but depends on a vulnerable identity database to stay consistent, while subject- and key-conditioned pseudonym generators remove that database but discard the original frame along with its context. This thesis closes the gap with SKPG-Swap: a hybrid framework in which a lightweight Subject- and Key-conditioned Pseudonym Generator (SKPG) derives a consistent pseudonym from a subject's face and a secret key, combined with a face-swap model which blends that pseudonym back into the original frame. Evaluated against bounding-box rendering strategies built on the same SKPG backbone, SKPG-Swap retains nearly all of the action-recognition accuracy of unmodified videos on UCF101 and outperforms the other pseudonymization methods on RAVDESS emotion recognition. A controlled experiment further shows that assigning a subject a consistent pseudonym identity, rather than an inconsistent one, results in more stable predictions across videos, motivating the consistency requirement.
Are we SMPLy biased
Identifying ethical biases in Action Recognition
Human Action Recognition (HAR) models are increasingly deployed in high-stakes environments, yet their fairness across different human appearances has not been analyzed. We introduce a framework for auditing bias in HAR models using synthetic video data, generated with full control over visual identity attributes such as skin color. Unlike prior work that focuses on static images or pose estimation, our approach preserves temporal consistency, allowing us to isolate and test how changes to a single attribute affect model predictions. Through controlled interventions using the BEDLAM simulation platform, we show whether some popular HAR models exhibit statistically significant biases on the skin color even when the motion remains identical. Our results highlight how models may encode unwanted visual associations, and we provide evidence of systematic errors across groups. This work contributes a framework for auditing HAR models and supports the development of more transparent, accountable systems in light of upcoming regulatory standards.
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Human Action Recognition (HAR) models are increasingly deployed in high-stakes environments, yet their fairness across different human appearances has not been analyzed. We introduce a framework for auditing bias in HAR models using synthetic video data, generated with full control over visual identity attributes such as skin color. Unlike prior work that focuses on static images or pose estimation, our approach preserves temporal consistency, allowing us to isolate and test how changes to a single attribute affect model predictions. Through controlled interventions using the BEDLAM simulation platform, we show whether some popular HAR models exhibit statistically significant biases on the skin color even when the motion remains identical. Our results highlight how models may encode unwanted visual associations, and we provide evidence of systematic errors across groups. This work contributes a framework for auditing HAR models and supports the development of more transparent, accountable systems in light of upcoming regulatory standards.