CNN-Mesh: Enhancing Face Recognition with Convolutional Neural Networks and Mesh Algorithm

Abstract

Several areas of AI have found uses for face recognition (FR) technologies, such as biometrics, authentication, security, surveillance, and law enforcement. Results from FR using deep learning (DL) models, especially CNNs, have been promising. By utilizing learning methods such as Convolutional Neural Networks (CNN) and sophisticated decoding algorithms to improve face recognition accuracy our goal is to ensure privacy protection while tackling issues related to varying lighting conditions and facial expressions. The proposed model (CNN-Mesh) is combined CNN algorithm with mesh technique for prediction the fully landmark of face. The combination has two stage: first stage is that the network collects feature extraction to solve the classification problem. Second stage apply mesh network after collects whole features for used it to describe the edge of faces. Moreover, it able the network to construct predictions based on those traits. supervised learning is the method that our model employs. During the process of learning, the model is instructed to recognize patterns and traits in the images that are associated with certain individuals. Our strategy involves the use of 68 Landmark markers to optimize facial recognition precision resulting in advancements in this field. The results of our CNN-Mesh algorithm achieved a 97% accuracy rate for classification and detecting faces, while the accuracy of the model is increased after mesh network to 99%.