In this research, implementation details and error analysis in deep learning-based face recognition mechanisms are explained to examine some recent advanced face recognition techniques with high level of accuracy and robustness. It uses the Labeled Faces in the Wild (LFW) dataset and applies extensive data augmentation including rotation, scaling, translation, and horizontal flipping of faces to augment the input face dataset for improved generalization. This research uses the pre-trained VGGFace model and fine tunes it with adding custom layers to achieve higher complexity and performance. Early stopping, model checkpointing and five-fold cross-validation is used during the training and validation process to facilitate best possible training without prone overfitting. ResultsThe results show that all proposed methods obtain great improvements for face recognition accuracy, with the best model reaching state-of-the-art performance (97.26% on LFW). This property - the ability of the model to cope well with pose, lighting and expression variations - seems to confirm that the advanced technical tricks used here are doing their job. By comparing the performance of the developed model with traditional methods and recent deep learning-based models, its increased efficiency is demonstrated. It is significant in the realm of face recognition as it offers a strong architecture which collates top deep learning models and precise preparation of data. I am beginning to work on integrating new datasets and experimenting with more complex architectures within a face recognition framework while also diving into how to use this responsibly.