The mask detection technology for occluded face analysis in the surveillance system

Area of Science:

• Computer Science
• Artificial Intelligence
• Biometrics

Background:

• Surveillance systems are crucial for security in public spaces like ATMs.
• Criminals often obscure faces with masks or helmets, hindering identification.
• Existing systems struggle with face occlusion, limiting their effectiveness.

Purpose of the Study:

• To develop an automatic mask detection technology for surveillance.
• To enhance face recognition systems by addressing face occlusion.
• To provide early warnings against intruders concealing their identities.

Main Methods:

• Utilizing Gabor filters for facial feature extraction.
• Employing geometric analysis algorithms for mask detection.
• Integrating mask detection with automatic face recognition.

Main Results:

• The proposed technology effectively detects masked faces.
• Demonstrated reliability and performance in experimental tests.
• Successfully assists in automatic face recognition systems.

Conclusions:

• The developed mask detection technology improves surveillance capabilities.
• It offers a solution to the challenge of face occlusion in security.
• The system enhances the overall effectiveness of automatic face recognition.