Toward automated face detection in thermal and polarimetric thermal imagery

Christopher Gordon; Mark Acosta; Nathan Short; Shuowen Hu; Alex L. Chan

doi:10.1117/12.2222578

17 May 2016 Toward automated face detection in thermal and polarimetric thermal imagery

Christopher Gordon, Mark Acosta, Nathan Short, Shuowen Hu, Alex L. Chan

Proceedings Volume 9842, Signal Processing, Sensor/Information Fusion, and Target Recognition XXV; 984212 (2016) https://doi.org/10.1117/12.2222578
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

Visible spectrum face detection algorithms perform pretty reliably under controlled lighting conditions. However, variations in illumination and application of cosmetics can distort the features used by common face detectors, thereby degrade their detection performance. Thermal and polarimetric thermal facial imaging are relatively invariant to illumination and robust to the application of makeup, due to their measurement of emitted radiation instead of reflected light signals. The objective of this work is to evaluate a government off-the-shelf wavelet based naïve-Bayes face detection algorithm and a commercial off-the-shelf Viola-Jones cascade face detection algorithm on face imagery acquired in different spectral bands. New classifiers were trained using the Viola-Jones cascade object detection framework with preprocessed facial imagery. Preprocessing using Difference of Gaussians (DoG) filtering reduces the modality gap between facial signatures across the different spectral bands, thus enabling more correlated histogram of oriented gradients (HOG) features to be extracted from the preprocessed thermal and visible face images. Since the availability of training data is much more limited in the thermal spectrum than in the visible spectrum, it is not feasible to train a robust multi-modal face detector using thermal imagery alone. A large training dataset was constituted with DoG filtered visible and thermal imagery, which was subsequently used to generate a custom trained Viola-Jones detector. A 40% increase in face detection rate was achieved on a testing dataset, as compared to the performance of a pre-trained/baseline face detector. Insights gained in this research are valuable in the development of more robust multi-modal face detectors.

Citation Download Citation

Christopher Gordon, Mark Acosta, Nathan Short, Shuowen Hu, and Alex L. Chan "Toward automated face detection in thermal and polarimetric thermal imagery", Proc. SPIE 9842, Signal Processing, Sensor/Information Fusion, and Target Recognition XXV, 984212 (17 May 2016); https://doi.org/10.1117/12.2222578

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available