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T.F.R. van Hoorn
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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.
Sample selection bias occurs when the selected samples in a subset of the original data set follow a different distribution than the samples from the original data set. This type of bias in the training set could result in a classifier being unable to predict samples from a testing data set optimally. Domain adaptation techniques try to adapt classifiers to a possible bias in the training or testing set. Subspace mapping techniques specifically do this by trying to find common subspaces between the source and target domain, where the source domain is the domain with all samples used for training, and the target domain is the domain with samples that must be predicted. This project aims to evaluate the effectiveness of two subspace mapping techniques in mitigating sample selection bias. This research assumes that no data samples from a target domain are available, but only unlabelled samples coming from an underlying global domain. The two subspace mapping techniques that will be tested in this paper are subspace alignment (SA) and transfer component analysis (TCA). This paper will show that the subspace alignment method is more effective on data sets with fewer features and where the source and target domains are further away from each other. The transfer component analysis method is more effective when more training samples are available on data sets with fewer features and where the distance between the source and target domain is not too big. The effectiveness of both methods also depends on the type and form of the data sets they are used on.
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Sample selection bias occurs when the selected samples in a subset of the original data set follow a different distribution than the samples from the original data set. This type of bias in the training set could result in a classifier being unable to predict samples from a testing data set optimally. Domain adaptation techniques try to adapt classifiers to a possible bias in the training or testing set. Subspace mapping techniques specifically do this by trying to find common subspaces between the source and target domain, where the source domain is the domain with all samples used for training, and the target domain is the domain with samples that must be predicted. This project aims to evaluate the effectiveness of two subspace mapping techniques in mitigating sample selection bias. This research assumes that no data samples from a target domain are available, but only unlabelled samples coming from an underlying global domain. The two subspace mapping techniques that will be tested in this paper are subspace alignment (SA) and transfer component analysis (TCA). This paper will show that the subspace alignment method is more effective on data sets with fewer features and where the source and target domains are further away from each other. The transfer component analysis method is more effective when more training samples are available on data sets with fewer features and where the distance between the source and target domain is not too big. The effectiveness of both methods also depends on the type and form of the data sets they are used on.