Research and Analysis of Facial Recognition Based on FaceNet, DeepFace, and OpenFace - Li Minghan (ITM Web of Conferences)
This study provides a comprehensive review of recent advancements in face recognition technology, focusing on deep learning models such as FaceNet, DeepFace, and OpenFace. The primary evaluation criterion is these models' ability to produce accurate facial embeddings, which are essential for reliable identification and verification. The findings demonstrate that these models significantly enhance recognition performance, particularly under challenging conditions such as varying lighting and occlusions. However, the study also identifies ongoing issues, including the need for efficient processing and reliance on large, annotated datasets. Future research should address these challenges by improving the efficiency and scalability of deep learning models. Additionally, expanding datasets to include a broader range of facial features will enhance model robustness in real-world applications. Exploring the integration of advanced technologies, such as sophisticated data augmentation techniques, will further boost the accuracy and adaptability of face recognition systems. These efforts are expected to advance the development of more versatile and reliable face recognition technologies.