A Review Paper Comparing the CNN, LBPH, and PCA Face Recognition Algorithms

This paper presents a comprehensive comparative study of the Local Binary Patterns Histogram (LBPH), Convolutional Neural Network (CNN), and Principal Component Analysis (PCA) algorithms in image analysis and recognition. Through a systematic methodology, the algorithms are evaluated and compared based on criteria such as time complexity, space complexity, accuracy, efficiency, and robustness. The study examines their underlying principles, strengths, and weaknesses, while analyzing their performance, scalability, and applicability across various image analysis tasks. The findings provide insights into the implications, potential use cases, and limitations of each algorithm, and highlight areas for future research. This comparative study serves as a valuable resource for researchers and practitioners, enabling informed decision-making in selecting appropriate algorithms for image analysis and recognition applications.