Deep Learning Approach for Face Recognition Applied in IoT Environment- Comprehensive Review

In numerous businesses and academic fields, recognition of faces is an ongoing concern for research. This is because facial recognition, in addition to its many practical applications in access control, bankcard recognition, searching mug pictures, security monitoring, and surveillance systems, is a crucial human trait required for establishing and maintaining interpersonal bonds. Face recognition is among the most significant uses of image analysis. Real concerns exist regarding an automated system's capacity to recognize faces on par with a human. Humans are very good at identifying familiar faces, but we have trouble doing so when we are presented with a lot of unfamiliar faces. Computers should be able to outperform human limitations due to their practically endless memory and processing capacity. Face recognition, which looks to be a good compromise between reality and cultural acceptance and achieves a reasonable balance between security and privacy, is one of the most important biomarkers. This involves exploring advanced deep learning architectures, devising effective feature extraction techniques, and optimizing real-time performance to cater to the requirements of IoT devices. Additionally, the study delves into critical considerations such as data privacy, security, and human interaction, aiming to strike a balance between technological innovation and ethical concerns. By examining these interconnected aspects, the research aims to provide a holistic solution that enhances face recognition capabilities in IoT applications, contributing to the seamless integration of this technology into a wide range of scenarios, from access control to personalized services, and beyond.