This review explores EEG-based face recognition, focusing on methodologies, feature extraction techniques, and classification methods. Recognizing familiar and unfamiliar faces is crucial for social interaction, with EEG capturing brain activity to provide objective insights. Critical neural responses like N170, P300, and P600 are discussed in terms of their role in face recognition. The review highlights traditional machine learning approaches, alongside advanced deep learning techniques. Despite advancements, challenges such as signal noise, individual variability, and limited dataset sizes persist. Future research directions include improved noise reduction, personalized models, and data augmentation, aiming to enhance the accuracy and applicability of EEG-based face recognition systems.