Machine Learning Approach for a Novel Facial Recognition System

A facial recognition system can be developed using a machine learning approach that involves data collection, preprocessing, feature extraction, model training, evaluation and testing, and deployment. The system can be trained on a large dataset of facial images using techniques such as PCA, LBP, or CNNs for feature extraction and SVM, Random Forest, or Neural Networks for model training. The performance of the system can be evaluated using a test set, and the system can be deployed in real-world scenarios. However, it is crucial to consider the ethical and privacy implications of facial recognition technology and implement appropriate safeguards to prevent misuse. The Eigenface, Fisherface, and LBPH (Local Binary Patterns Histogram) algorithms are three popular techniques for face recognition in the OpenCV library. This work evaluates the performance of each algorithm on a specific dataset to determine which algorithm is the most appropriate for this application.