Optimizing Face Embedding Sizes and Accuracy in Facial Recognition Systems

Face recognition technology is integral to security, access control and identity verification in finance, healthcare and transportation.It protects personal data, secures online transactions, controls access to areas, and helps prevent identity theft.This paper proposes a novel hybrid optimization algorithm, Moss Particle Swarm Optimization (MPSO) to perform hyperparameter tuning, aiming to identify the neural network, alongside Triple Loss metrics for efficient recognition.The proposed method is tested with the Labeled Faces in the Wild (LFW) dataset, demonstrating its effectiveness in improving facial recognition systems.