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Summary
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 I will present recent works on face analysis, where we have designed deep convolutional neural networks (CNNs) targeted to extract attributes, emotions, as well as the more complex state of apathy from face videos. While a large body of work has aimed at extracting and classifying such information from faces, currently the inverse problem — namely face generation — has received increased attention. In this context, I will talk about our recent designed generative models, which allow for realistic generation of face images and videos, and the related deepfake detection.
Short Biography
Antitza Dantcheva (Ph.D. ’11 with Télécom ParisTech/Eurecom) is permanent researcher at the STARS team, Inria, Sophia Antipolis – Méditerranée. Previously, she was a Marie Curie fellow at Inria and a Postdoctoral Fellow at the Michigan State University and the West Virginia University, USA. In 2017 she received the French National Research Agency (ANR) JCJC young resea
rcher grant for her research project « ENVISION. Automated holistic human analysis ». 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).
The presentation will be held in English on BBB
