Facial classification from multispectral imagery through atmospheric turbulence using machine learning

Abu Bucker Siddik; Steven Sandoval; David Voelz; Laura E. Boucheron; Luis Varela

doi:10.1117/12.2677910

3 October 2023 Facial classification from multispectral imagery through atmospheric turbulence using machine learning

Abu Bucker Siddik, Steven Sandoval, David Voelz, Laura E. Boucheron, Luis Varela

Proceedings Volume 12693, Unconventional Imaging, Sensing, and Adaptive Optics 2023; 1269306 (2023) https://doi.org/10.1117/12.2677910
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Abstract

Facial classification has numerous real-world applications in various fields such as security and surveillance. However, images collected at long range through the atmosphere exhibit spatially and temporally varying blur and geometric distortion due to turbulence; consequently, making facial identification challenging. A multispectral facial classification approach is proposed utilizing machine learning for long-range imaging. A method for simulating turbulence effects is applied to a multispectral face image database to generate turbulence-degraded images. The performance of the machine learning method for this classification task is assessed to explore the effectiveness of multispectral imaging for improving classification accuracy over long ranges.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Abu Bucker Siddik, Steven Sandoval, David Voelz, Laura E. Boucheron, and Luis Varela "Facial classification from multispectral imagery through atmospheric turbulence using machine learning", Proc. SPIE 12693, Unconventional Imaging, Sensing, and Adaptive Optics 2023, 1269306 (3 October 2023); https://doi.org/10.1117/12.2677910

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