Radial basis function Artificial Neural Strategy with Dimensionality Reduction Algorithms Intelligent Fusion Method for Student Attendance Monitoring
Keywords:
Radial Basis Function (RBF), Preprocessing, Gaussian Filter, Classification, IOT Internet of ThingsAbstract
Image processing is the act of applying various image analysis to identifying task and improve the collected of data. Student attendance monitoring is described as the malpractice shown in a biometric system for identifying unauthorized students as the same actual consumer across original biometric or RFID Tag to identity today's number of present. Face images contain different regions based on skin color, face size, and camera quality. Nowadays, many face recognition methods are used for locker security and phone lock applications. The previous Method, the Convolutional Neural Network (CNN) method, was developed to classify the image and identify whether the image was a Spoof or not. Still, those methods have some drawbacks, such as identifying the separate parts of the region and blurring images. So this work introduces the combination of Machine learning based Radial Basis Function (RBF) for classification of each student and performance of the classroom of student attendance monitoring. The image Preprocessing using the Gaussian Filter identifies the desired region with a noise-free image, and the proposed filter calculates the values for a clear structure image. The segmentation separates the different regions like skull, skin, and Background with applied Radon Transform, this transforms technique identifies the complex pixel from different skull sizes of input images. The final step is the Classification of Radial Basis Function (RBF) using Dimensionality Reduction Algorithms to classify the image based on RGB components. Radial basis function dimensional reduction Algorithms for a clear view of a specific image with a specific name of each student of each classroom. The student data was continuously monitored based on Name and role number using IOT (Internet of Things). The performance of the proposed method is validated and developed through simulation using the mat lab software based on that accuracy specificity is developed in future processes.
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