Coupled bias-variance tradeoff for cross-pose face recognition.

Area of Science:

• Computer Vision
• Machine Learning
• Pattern Recognition

Background:

• Face recognition across varying poses remains a significant challenge in computer vision.
• Subspace-based face representation is often framed as a regression problem.

Purpose of the Study:

• To propose a novel approach for cross-pose face recognition using regression.
• To enhance the stability and accuracy of face recognition systems when dealing with pose variations.

Main Methods:

• Revisiting subspace-based face representation as a regression problem.
• Developing a regressor with a coupled bias-variance tradeoff for improved cross-pose representation.
• Exploring ridge regression and lasso regression techniques.

Main Results:

• Achieving a coupled balance between bias and variance in regression enhances cross-pose face representation.
• The proposed method demonstrates increased stability against pose differences.
• Significant improvements in recognition performance were observed on benchmark datasets.

Conclusions:

• A coupled bias-variance tradeoff in regression is effective for improving cross-pose face recognition.
• The developed approach offers a robust solution for handling pose variations in face recognition systems.
• Experimental validation on CMU PIE, FERET, and Multi-PIE datasets confirms the efficacy of the proposed method.