Innovative Integration of Convolutional Neural Networks for Enhanced Face Recognition

Abstract

The face Recognition technique is important in Computer Vision nowadays. The research study focuses on a Face Recognition system that uses deep learning to identify face photos. Face detection and categorization are carried out using various Convolutional neural network (CNN) models using deep learning methods. Prior research has mostly focused on either the ResNet or DenseNet-based CNN models. The current study combines ResNet and DenseNet to create a hybrid model. The suggested work aims to improve efficiency and accuracy. During the simulation's training and testing phases, categories are taken into account. The present study is centered on the Labeled Faces in the Wild (LFW) dataset. The photos go through an initial noise reduction procedure. Picture quality assessment involves considering measures like MSE, PSNR, and SSIM. Once the suggested model has completed training, it produces high-quality images. The suggested system includes the Innovative CNN approach framework, which combines DenseNet with a noise reduction approach, a segmentation mechanism, and a ResNet model based on CNN. Comparative research was performed to assess the precision of several filtered image collections using different convolutional neural network models. The simulation results show that the proposed model had higher performance and accuracy than traditional ResNet and DenseNet models.