Neural Network Based Face Recognition System for Students

Abstract

Face recognition systems are part of biometric information processing. Face recognition is easier to apply and requires a longer work area than scanning fingerprints, irises, signatures, etc. Due to the variety of facial expressions, lighting effects, and background complexity of images, taking real-world data-based facial recognition, photographs, sensor images, and database images can be a daunting task. Face recognition is one of the triumphant and important uses of image processing. This research describes facial recognition methods and algorithms developed by various researchers with the help of convolutional neural networks (CNNs) and applied in the fields of image processing and pattern recognition. This research will also describe how CNN will be utilized for face recognition and how it is more successful than other approaches. There is a slew of CNN-based algorithms that provide a high-level overview of facial recognition. Accordingly, this study includes a comprehensive review of facial recognition experiments and systems based on different techniques and algorithms from CNN. Here in this research project, we proposed a facial recognition system through convolutional neural networks. In the field of computer vision, neural networks are a set of algorithms that seek to discover the fundamental connections of a dataset through a process. Our approach to the face recognition problem involves combining principal components and neural networks. We describe the app on vertically oriented front-facing views of human faces specifically designed for universities to recognize their students.