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Multiview face detection and registration requiring minimal manual intervention.

Seyed Mohammad Hassan Anvar¹, Wei-Yun Yau, Eam Khwang Teoh

¹School of Electrical and Electronic Engineering,Nanyang Technological University and Institute for Infocomm Research, Singapore. seye0005@e.ntu.edu.sg

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 24, 2013

Summary

This study introduces a novel face constellation for simultaneous multiview face detection and localization. This method requires minimal manual training, improving accuracy for faces with arbitrary poses.

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Area of Science:

Computer Vision
Machine Learning
Pattern Recognition

Background:

Traditional face recognition systems necessitate pre-detection and localization.
Handling multiple faces with varying views and scales presents a significant challenge.

Purpose of the Study:

To propose a novel approach for simultaneous detection and localization of multiple faces with arbitrary views and scales.
To introduce a face constellation method that minimizes manual training data requirements.

Main Methods:

Development of a face constellation for multiview face detection and localization.
Automatic registration of training images to a reference image using distinctive local features.
A probabilistic classifier for evaluating face correspondences and identifying distinctive points amidst false matches.

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Main Results:

The proposed face constellation method achieves accurate multiview face detection and localization.
Minimal manual intervention is required for training, unlike other multiview approaches.
Experimental results on FERET, CMU, and FDDB datasets demonstrate superior performance over state-of-the-art methods for arbitrary pose face detection.

Conclusions:

The face constellation approach offers an efficient and effective solution for multiview face detection and localization.
This method significantly reduces the need for extensive labeled training data.
The approach shows strong performance in detecting faces across diverse and challenging poses.