The Speaker is: Prof. Ioannis Pitas (Professor at the Department of Informatics of AUTH and Director of the Artificial Intelligence and Information Analysis (AIIA) lab)

Abstract:  Privacy protection is a very important issue, in the context of social media and GDPR. This lecture overviews the face de-identification problem from an engineering perceptive. In principle, face de-identification methods aim on calculating an affine or a non-linear transformation to an input facial image, so that the depicted person identity is no longer recognized by humans or automated human analysis tools. Traditional applications in the media mainly involve applying additive noise (e.g., pixilation, blurring) or reconstruction-based techniques on the facial image region, achieving sufficient de-identification performance at the expense of corroding image quality. Recently proposed deep learning-based generative methods for face de-identification promise excellent de-identification performance against automated tools while producing visually pleasing yet still not useful images for the human viewers. Finally, adversarial-based face de-identification methods optimally generate the minimum required additive noise that disables automated face detection/recognition systems, thus the de-identified images maintain maximal utility for human viewers.

Bio:

Prof. Ioannis Pitas (IEEE fellow, IEEE Distinguished Lecturer, EURASIP fellow) received the Diploma and PhD degree in Electrical Engineering, both from the Aristotle University of Thessaloniki (AUTH), Greece. Since 1994, he has been a Professor at the Department of Informatics of AUTH and Director of the Artificial Intelligence and Information Analysis (AIIA) lab. He served as a Visiting Professor at several Universities.

His current interests are in the areas of computer vision, machine learning, autonomous systems, intelligent digital media, image/video processing, human-centred computing, affective computing, 3D imaging and biomedical imaging.