Detecting Faces in Impoverished Images
Abstract
The ability to detect faces in images is of critical ecological significance. It is a pre-requisite for other important face perception tasks such as person identification, gender classification and affect analysis. Here we address the question of how the visual system classifies images into face and non-face patterns. We focus on face detection in impoverished images, which allow us to explore information thresholds required for different levels of performance. Our experimental results provide lower bounds on image resolution needed for reliable discrimination between face and non-face patterns and help characterize the nature of facial representations used by the visual system under degraded viewing conditions. Specifically they enable an evaluation of the contribution of luminance contrast, image orientation and local context on face-detection performance.
Document Details
- Document Type
- Technical Report
- Publication Date
- Nov 01, 2001
- Accession Number
- ADA636815
Entities
People
- Antonio Torralba
- Pawan Sinha
Organizations
- Massachusetts Institute of Technology