#20.01 - Biometrics

• "What’s in a Face? Computer Vision for Face Analysis and Generation", Antitza Dantcheva (INRIA-STARS team)

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 observations lead to the tantalizing question: “What’s in a Face?”

In my talk I will firstly provide a brief overview of the research landscape in face analysis and generation. This area, over the last years, has witnessed a tremendous progress due to deep convolutional neural networks (CNNs). I will then zoom into recent works on face analysis, where we have used face images and videos to deduce attributes, emotions, as well as the more complex state of apathy.
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.

Papers:
Antitza Dantcheva Francois Bremond Piotr Bilinski (2018): "Show me your face and I will tell you your height, weight and body mass index", ICPR 2018

Happy, Dantcheva, Das, Zeghari, Robert, and Bremond (2019): "Characterizing the State of Apathy with Facial Expression and Motion Analysis", FG 2019

Wang, Bilinski, Bremond, Dantcheva (2020): "ImaGINator: Conditional Spatio-Temporal GAN for Video Generation", WACV 2020