Performance Evaluation of Selected Thermal Imaging-Based Human Face Detectors

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 578)


The paper is devoted to the problem of face detection in thermal imagery. Its aim was to investigate several contemporary general-purpose object detectors known to be accurate when working in visible lighting conditions. Employed classifiers are based on AdaBoost learning method with three types of low-level descriptors, namely Haar–like features, Histogram of Oriented Gradients, and Local Binary Patterns. Additionally, the performance of recently proposed Max-Margin Object-Detection Algorithm joint with HOG feature extractor and Deep Neural Network-based approach have been investigated. Performed experiments, on images taken in controlled and uncontrolled conditions, gathered in our own benchmark database and in a few other databases support final observations and conclusions.


Thermovision Biometrics Face detection Haar–like features Histogram of Oriented Gradients Local Binary Patterns AdaBoost Max-Margin Object-Detection Algorithm Deep Neural Network 


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© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of Technology, SzczecinSzczecinPoland

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