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Title
Abstract
FAQs
Introduction
Thermal Face Recognition Methods
Thermal Face Image Datasets
Discussion
Conclusion
References
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Computer Science

An overview of thermal face recognition methods

Profile image of Marina Ivasic-KosMarina Ivasic-Kos

2018, 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

https://doi.org/10.23919/MIPRO.2018.8400200
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Abstract

The popularity of surveillance systems grows as well as a need for better security systems particularly in a bad lighting conditions or at night. The aim of a security system is to collect as many details as possible to enable a better recognition of persons. In this paper, a comparison of representative thermal face recognition methods will be given, emphasizing their strengths and weaknesses. Then, trends in the development of surveillance and security systems will be outlined such as fusion of visible and thermal images and use of convolutional neural networks. Also, existing challenges of thermal facial recognition and its applications in a real world will be pointed out.

FAQs

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What explains the success of SIFT descriptors in thermal face recognition?add

The study shows that SIFT descriptors outperform other methods, achieving up to 96% recognition accuracy on SWIR images. This indicates their effectiveness in capturing thermal facial features, particularly in varying conditions.

How do thermal face biometrics handle illumination changes during recognition?add

Thermal face biometrics demonstrate robustness to illumination changes, operating effectively in dark environments. Studies show that accuracy remains high even under various lighting conditions, unlike traditional visual methods.

What are the limitations of using thermal imaging for face recognition?add

Limitations include sensitivity to glass, high noise levels, and decreased accuracy at greater distances. The recognition rate notably drops beyond 90 meters, highlighting distance as a critical factor.

When did significant advancements in thermal face recognition methods occur?add

Recent developments have transpired over the last five years, emphasizing deep learning and CNN architectures. For instance, CNN methods have achieved accuracy rates exceeding 98% in structured experiments.

Why is the use of multi-modal approaches important in thermal face recognition?add

Multi-modal approaches enhance recognition accuracy by integrating thermal and visible images, addressing individual weaknesses. This integration has led to improved identification rates, with reported success rates reaching 90%.

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