Deep Learning-Based Automatic Student Authentication

Introduction

Humans have a certain amount of time in which to fulfill their goals. Spending time on mindless, mindless physical labour is a waste of time and a distraction from more important matters. Using Deep Neural Networks to solve the mass surveillance issue, offer an automation-based strategy to avoid such pointless effort, focusing primarily on the sub problem of enhancing teacher efficiency by automating the attendance taking process (Girshick et al., 2014). Intensely complex neural networks Using Deep Learning frameworks for face detection and recognition, this chapter proposes ways to lessen the burden on teachers by delivering fast and accurate results while also overcoming existing method shortcomings like multiclass identification problems, multiple appearances of the same person, illumination variance, and occlusion (Ren et al., 2015). Using deep learning, data is routed through several layers of non-linear mappings before the computer can construct a representation for it. Finally, Deep Learning Models are nothing but a function approximator capable of learning each and every function that depends on the input dataset. In the end, An extensive variety of subjects is included under the umbrella term “deep learning,” which includes anything from supervised regression models to reinforcement learning-based AI. In Deep Learning, the problem of face detection and recognition is extremely challenging and contentious (Redmon et al., 2016). Face recognition on a broad scale has proven difficult due to a scarcity of properly labelled training data. Current state-of-the-art methods have been pushed aside by Nvidia Graphics cards, which can perform matrix operations very quickly. For a long time, Image and Vision Computing was dominated by non-deep learning algorithms as Viola Jones (Viola and Jones, 2004). Clustering and HOG, but noise in the data, such as occlusion and luminance variation, has greatly hindered these methods. Before Deep Learning, only Feature Engineering, Data Preprocessing (Zhu et al., 2006), Output Parsing, and KDD Tree Structures could provide an acceptable result in face recognition. RCNN, Faster and Faster RCNN, Overfeat, and Yolo can now operate in near-real time on commercial workstations because of the improved processing capability. The application of Deep Learning techniques for image and vision computing skyrocketed with the invention of Convolutional Neural Networks (Ding et al., 2014). For example, in Deep Learning, the use of data augmentation procedures during training may ensure that the model is invariant to noise and has a good generalizability so that it can overcome challenges with noise and erroneous predictions caused by things like occlusion.

There are two parts to a Face Recognition challenge in general: detecting a face in the picture and accurately classifying the image (Krizhevsky et al., 2012). To put it another way, we're dealing with an issue with the detection and classification of objects. How can an object detection framework be described? First, the neural network must be able to locate the item to be identified in an image, and secondly, it must be able to predict with high certainty that this object belongs to a certain class. There are a variety of ways to solve the issue, but the two most common are branch networks and a single network. Multiple Neural Network topologies are used in Branch Networks to conduct the many subtasks necessary to complete the pen-ultimate task, as the name indicates In nature, the branching sub-networks are decoupled, and each of them has been taught to mimic a unique function (Liu et al., 2016). Subnetworks that focus on the issue of region proposal are used in object detection networks. This means that the network is trying to learn which area in the picture is most likely to contain an item of interest. Classification of the observed item is taught to the second branching sub-network. The proposal of (Lowe, 2004) R-CNN with deep learning features a rich hierarchical feature set for accurate object detection and semantic segmentation. Region extraction is the initial phase of R-CNN, which may be done using approaches like objectness and selective search, limited parametricmin cuts, and so on. Selective search has been utilised to obtain region ideas for all of the experiment's goals.