Efficient Face Detection and Recognition with PCA and Eigenfaces: A Comprehensive Analysis Md. Ibrahim Abdullah

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, discriminative 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 tradeoff between dimensionality reduction and recognition accuracy. Experimental results demonstrate that the PCA-based approach achieves high recognition accuracy while maintaining computational efficiency, making it suitable for resourceconstrained 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.