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Cerenkov Luminescence Imaging of Interscapular Brown Adipose Tissue

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Published on: October 7, 2014

Intrinsic illumination subspace for lighting insensitive face recognition.

Chia-Ping Chen¹, Chu-Song Chen

¹Institute of Information Science, Academia Sinica, Taipei 115, Taiwan. cpchen@iis.sinica.edu.tw

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|October 5, 2011

Summary

This study introduces an intrinsic illumination subspace for lighting-insensitive face recognition. This method enables accurate facial recognition under varying light conditions using a single image.

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Area of Science:

Computer Vision
Machine Learning
Image Processing

Background:

Face recognition systems often struggle with variations in illumination.
Existing methods may require multiple images or complex preprocessing.

Purpose of the Study:

To develop a lighting normalization technique for robust face recognition.
To introduce the intrinsic illumination subspace for handling illumination variations.

Main Methods:

Constructing an intrinsic illumination subspace from intrinsic images.
Developing a class-based generic subspace for efficient representation.
Proposing a single-image lighting normalization method.

Main Results:

Demonstrated the effectiveness of the proposed lighting normalization method.
Verified the applicability of the class-based generic subspace to novel objects.
Achieved lighting-insensitive face recognition with a single input image.

Conclusions:

The intrinsic illumination subspace provides an efficient representation for illumination variations.
The proposed method offers a fast and simple solution for real-time face recognition.
The approach is suitable for resource-constrained applications like mobile devices.