Removal of Noises in IOT Based Imaging for Optimized Face Recognition
The random change of brightness or colour information in pictures is referred to as image noise. Errors in picture acquisition, transmission, and processing result in noise. The true intensities of the real scene are not reflected in the noise-corrupted picture. The ultimate goal of picture restoration is to improve an image to a certain level of quality. Face detection is a computer technique that recognises human faces in digital pictures and is used in a number of applications. When a picture is in excellent quality and free of random noise, face detectors function well. The objective of this study is to see how adding extra training data to a convolution neural network-based face recognition system affects its performance. The face detector is built with the Caffe deep learning model, and the Alex Net model is utilised. When more training information is provided, the operating frequency increases.