Face Recognition with 2D Principal Component Analysis and Support Vector Machine

We propose a novel methodology to address facial recognition challenges by combining Two-Dimensional Principal Component Analysis (2DPCA) with Support Vector Machine (SVM) classification. The 2DPCA implementation directly processes image matrices without vectorization, preserving spatial relationships while reducing computational complexity through covariance matrix analysis. SVM employs kernel functions (e.g., RBF or polynomial) to establish optimal hyperplanes for pattern separation in high-dimensional feature space. Our experimental framework includes database validation with comparative performance metrics. Results indicate our hybrid approach significantly enhances classification accuracy by 15-25% compared to conventional methods. Further analysis explores dataset adaptability across varying illumination conditions and facial expressions, suggesting parameter optimization techniques for cross-dataset applications. We propose architectural improvements including parallel processing for real-time implementation and adaptive kernel selection mechanisms. This research contributes to advancing facial recognition systems through computationally efficient feature extraction and mathematically robust classification boundaries.