Postdoctoral Project: Synthesizing knowledge-augmented face perception

Part of research project: Knowledge-augmented face perception

Principal Investigators

Description of the postdoctoral project

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.

 

Project start date: October 1, 2019

Prerequisites

Applicants must hold a PhD degree in Computer Science, Computer Engineering or related engineering disciplines and should have proven skills/background in following topics:

  • Computer vision and image analyses
  • In depth programming skills (C/C++, Python)
  • Laboratory experience using cameras and robotic camera platforms
  • Strong interest in psychology, visual perception and machine learning

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