Robust face recognition using quaternion interval type II fuzzy logic-based feature extraction on colour images.

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Traditional face recognition methods often overlook crucial color information and pixel interdependencies in color images.
• Existing techniques lack efficient processing of multi-channel color data, leading to potential redundancy and reduced accuracy.

Purpose of the Study:

• To develop a robust and fast learning technique for face recognition by integrating quaternion numbers, interval type II fuzzy logic, and a deterministic learning machine (DLM).
• To address the limitations of traditional methods by incorporating color information and pixel associations more effectively.

Main Methods:

• A novel technique, quaternion interval type II based deterministic learning machine (QIntTyII-DLM), is proposed.
• Color face images are represented using quaternion number sequences to capture interrelationships between Red, Green, and Blue (RGB) channels.
• Quaternion representations are fuzzified using interval type II fuzzy logic to reduce pixel redundancy and transform channels into an orthogonal color space.
• Classification is performed using a non-iterative, parameter-free deterministic learning machine (DLM).

Main Results:

• The proposed QIntTyII-DLM technique demonstrates improved performance on standard face datasets (AR, Georgia Tech, Indian face (female), and faces 94 (male)).
• The method achieves a reduction in percentage error rate by approximately 10-12% compared to existing techniques.
• Significant improvements in computational speed were observed.

Conclusions:

• The integration of quaternion numbers and interval type II fuzzy logic with DLM offers a superior approach to face recognition.
• The QIntTyII-DLM method effectively utilizes color information and reduces pixel redundancy, leading to enhanced accuracy and efficiency.
• This technique presents a promising advancement in robust and fast face recognition systems.