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Updated: Jun 19, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Using stereo matching with general epipolar geometry for 2D face recognition across pose.

Carlos D Castillo¹, David W Jacobs

¹Department of Computer Science, University of Maryland, College Park, MD 20742, USA. carlos@cs.umd.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence

|October 17, 2009

Summary

This study introduces a novel stereo matching approach for robust face recognition across varying poses. The method proves effective even with significant pose and lighting changes, outperforming existing techniques.

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Last Updated: Jun 19, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Area of Science:

Computer Vision
Biometrics
Pattern Recognition

Background:

Pose variation presents a significant challenge in automated face recognition systems.
Existing methods often struggle with maintaining accuracy when facial images are captured from different viewpoints.

Purpose of the Study:

To develop a robust face recognition method that is invariant to pose variations.
To leverage stereo matching techniques for assessing facial similarity across different poses.

Main Methods:

Utilizing stereo matching to establish correspondences between 2D face images acquired from different poses.
Computing epipolar geometry from three or four feature points in face images.
Adapting a stereo matching algorithm to quantify face similarity.

Main Results:

The stereo matching cost effectively measures face similarity, demonstrating insensitivity to pose variations.
The proposed method achieves superior performance on the CMU PIE dataset compared to existing approaches.
The technique shows robustness against variations in lighting conditions.

Conclusions:

Stereo matching offers a powerful and robust solution for face recognition across pose variations.
The developed approach enhances the reliability of face recognition systems in real-world scenarios.
This method provides a significant advancement in handling pose and lighting challenges in biometrics.