Knowledge-augmented face perception

Face perception and categorization is fundamental to social interactions. In humans, input from facial features is integrated with top-down influences from other cognitive domains, such as expectations, memories and contextual knowledge. In contrast to human perception, automatic systems of face processing are typically based purely on bottom-up information without considering factors as prior knowledge. The aim of this project is therefore to bridge the gap between human and synthetic face processing by integrating top-down components typical for human perception into synthetic systems. The results of experiments involving human subjects in combination with video recordings will be used in deep learning training procedures aiming at the development of computational models.

Principal Investigators
Abdel Rahman, Rasha Prof. Dr. rer. nat. (Details) (Neuralcognitive Psychology)

Financer
DFG-Exzellenzinitiative: Cluster

Duration of Project
Start date: 10/2019
End date: 09/2022

Related umbrella project
01/2019 - 12/2025

Last updated on 2020-01-06 at 19:52