The topic of human facial analysis has engaged researchers in multiple fields including computer vision, biometrics, forensics, cognitive psychology and medicine. Interest in this topic has been fueled by scientific advances that suggest insight into a person’s identity, intent, attitude, as well as health; all solely based on their face images and videos. The above leads to the tantalizing question: “What’s in a Face?”
In this talk Antitza will present recent works on face analysis, where the team has designed deep convolutional neural networks (CNNs) targeted to (a) cross spectral face recognition, as well as (b) analyse the complex state of apathy from face videos. While a large body of work was aimed at extracting and classifying such information from faces, currently the inverse problem — namely face generation — has received increased attention. In this context, she will talk about recent designed generative models, which allow for realistic generation of face videos, and the related deepfake detection.
Curriculum vitæ
Antitza Dantcheva is a Research Scientist with the STARS team of INRIA Sophia Antipolis, France. Previously, she was a Marie Curie fellow at INRIA and a Postdoctoral Fellow at the Michigan State University and the West Virginia University, USA. She received her Ph.D. degree from Télécom ParisTech/Eurecom in image processing and biometrics in 2011. Her research is in computer vision and specifically in designing algorithms that seek to learn suitable representations of the human face in interpretation and generation. She is recipient among others of the Best Poster Award at IEEE FG 2019, winner of the Bias Estimation in Face Analytics (BEFA) Challenge at ECCV 2018 (in the team with Abhijit Das and Francois Bremond) and Best Paper Award (Runner up) at the IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2017).
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