Bilateral 2DLDA for Face Recognition

This implementation presents a bilateral 2DLDA (Two-Dimensional Linear Discriminant Analysis) code specifically designed for face recognition tasks. While most existing 2DLDA implementations typically employ unilateral approaches, this contribution offers a bilateral variant for reference and comparative study. The bilateral 2DLDA algorithm employed in this code demonstrates superior accuracy and robustness compared to traditional unilateral 2DLDA methods by performing feature extraction along both row and column directions of image matrices. Key implementation aspects include dual projection matrix computation, bidirectional feature dimension reduction, and enhanced class separability through simultaneous row-column optimization. By sharing this bilateral 2DLDA implementation, I aim to provide researchers and developers with an alternative methodological approach and reference point for advanced face recognition systems. The code structure facilitates easy integration with standard face recognition pipelines while offering improved performance characteristics. I hope this contribution proves valuable and beneficial for those working in pattern recognition and computer vision domains.