Influencing Factors in Facial Recognition and Verification: A Siamese Neural Network Hyperparameter Study

There are many practical uses for facial verification and recognition jobs. In this study, we look into the developments and difficulties faced in facial recognition technologies, especially in the application of Siamese neural networks. This study converges around optimizing hyperparameters to improve Siamese network accuracy and performance. We evaluated the impact of different optimizers, loss functions, and data preprocessing techniques on Siamese network performance through a chain of experiments. The results demonstrated the importance of choosing the right hyperparameters, demonstrating how decisions such as the Adam optimizer and Binary Cross-Entropy as the loss function may greatly enhance the system's capability to precisely identify and validate faces. This study also overlooks the importance of diverse and high-quality datasets and states how these elements affect the model's performance. With this work, we hope to give a thorough manual for fine-tuning Siamese neural networks for use in facial recognition applications, along with helpful advice on how to overcome the difficulties posed by dataset variability and hyperparameter selection. In addition to adding to the theoretical knowledge of facial verification technologies, our research offers useful suggestions for enhancing the conception and application of these systems in actual settings.