Supervised And Unsupervised Neural Network Deep Analysis For Facial Recognition

In this technological era automation is playing a vital role in making things faster. Face Recognition is one of the examples of automation. Facial Recognition is categorized as a biometric method alongside fingerprint scan and iris scan. Back in the days facial recognition is only limited for safety and security purposes but nowadays it is widely used in various industries like Banking, Healthcare, and many more. One such industry is the educational industry. Various universities around the globe have already implemented biometrics through facial recognition not only for the staff but also for recording the student's attendance. Currently available Facial Recognition systems are able only able to track one person at an instance. The constructed system must track the individual attendance while he/she being recorded in a live video or while they were in a group photo using machine learning techniques. The system must analyze each individual in a group using the image provided and mark the individual's basic information like name, class, section, etc. In general, the system is trained with each individual's 180-degree facial view with the help of convolutional neural networks. And to improve accuracy the system must be trained using deep learning and deploying with a huge and distinct dataset. For achieving this we have conducted a study comparing five well-established algorithms to know which algorithm is reliable