Efficient Face Detection and Recognition with PCA and Eigenfaces: A Comprehensive Analysis

Face detection and recognition are critical tasks in computer vision with applications in security systems, biometric authentication, and human-computer interaction. This paper presents a comprehensive study leveraging Principal Component Analysis (PCA) and Eigenfaces for efficient dimensionality reduction and compact, discrimina-tive facial feature representation. The study introduces a robust pipeline integrating preprocessing, feature extraction, and efficient training. Using the CelebA dataset for training and the LFW dataset for evaluation, the system addresses real-world challenges, including variations in lighting, expressions, and poses. The performance is analyzed across configurations, exploring the trade-off between dimensionality reduction and recognition accuracy. Experimental results demonstrate that the PCA-based approach achieves high recognition accuracy (95% on controlled datasets) while maintaining computational efficiency, making it suitable for resource-constrained environments. The findings highlight the system’s robustness, scalability, and practical applicability in both constrained and real-time scenarios. This work concludes with an analysis of strengths and limitations and offers recommendations for integrating non-linear techniques and advanced learning models to further enhance scalability, accuracy, and real-world performance.