De-identifying Face Image Datasets While Retaining Facial Expressions

Andreas Leibl, Andreas Meißner, Stefan Altmann, Andreas Attenberger, Helmut Mayer

Abstract: Progress in computer vision, particularly based on machine learning, depends heavily on the availability of appropriate datasets. However, for applications like face recognition or emotion detection, this requires the collection of face images, which comprise especially privacy-sensitive biometric data. The corresponding valid ethical concerns and legal regulations regarding privacy rights limit the creation of new datasets. To reconcile the need for detailed facial image datasets with the right to privacy protection, appropriate anonymization techniques are needed. To this end, we suggest a pipeline to repurpose a face swapping tool for de-identification by combining it with synthetic image generation and a novel procedure to select source images to improve the trade-off between data utility retention and privacy enhancement. A quantitative comparison of our results to other de-identification approaches shows that our method leads to better retention of facial expressions while providing adequate privacy protection. Thus, applying this procedure to face image datasets before publication could help mitigate privacy concerns.