Work

Automatic Eye Detection Error as a Predictor of Face Recognition Performance

Year: 2014

Type: conference-paper

Abstract: Various facial image quality parameters like pose, illumination, noise, resolution, etc are known to be a predictor of face recognition performance. However, there still remain many other properties of facial images that are not captured by the existing quality parameters. In this paper, we propose a novel image quality parameter called the Automatic Eye Detection Error (AEDE) which measures the difference between manually located and automatically detected eye coordinates. Our experiment results carried out using FaceVACS recognition system and the MultiPIE dataset show that AEDE is indeed a predictor of face recognition performance.

Source: University of Twente Research Information

Authors: Abhishek Dutta, Raymond Veldhuis, Luuk Spreeuwers

Institution: University of Twente

Language: English

Topic: Face recognition and analysis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, University of Twente, FP7 People: Marie-Curie Actions

Award: Bayesian Biometrics For Forensics

OpenAlex ID: W127332220