Thermal Infrared Face Recognition.

Area of Science:

• Computer Vision
• Biometrics
• Artificial Intelligence

Background:

• Deep learning technology for thermal infrared face recognition is increasingly accessible for research.
• This accessibility has spurred significant advancements and novel findings in the field.
• Thermal infrared imaging offers unique advantages over visible light for face recognition.

Purpose of the Study:

• To review previous research in thermal infrared face recognition.
• To highlight the capabilities of thermal infrared face recognition, including recognizing challenging faces and mapping facial vasculature.
• To provide a comprehensive overview of the current state of the art.

Main Methods:

• Review of existing literature on thermal infrared face recognition.
• Analysis of studies focusing on temperature variations, mathematical models, and wave properties.
• Examination of various methodologies employed in thermal infrared face recognition.

Main Results:

• Deep learning has significantly enhanced the capabilities of thermal infrared face recognition.
• Thermal imaging can overcome limitations of visible light recognition, such as poor illumination.
• Facial blood vessel structure can be identified, offering a unique biometric feature.

Conclusions:

• The field of thermal infrared face recognition is rapidly evolving due to advancements in deep learning.
• This technology holds promise for improved security and identification systems.
• Further research into temperature variations and advanced algorithms will continue to drive progress.