Dataset Infant Anonymization with Pose and Emotion Retention

We demonstrate a procedure for the anonymization of infant subjects in videos such that salient behavioral information is retained. This method also creates a new identity that is consistent temporally across video frames. We present an overview of this anonymization process, which involves moving through the latent space of a generative model with an infant specific latent space traversal technique. We apply the technique on videos of infants, a historically difficult source of data, and make comparisons to other state-of-the-art anonymization systems. Metrics demonstrate an improved ability to retain emotional content of videos during the anonymization process, even during extreme emotions or poses, while maintaining a consistent identity throughout.