Deep Learning For Face Recognition: Ai-Powered Solutions With Convolutional Neural Networks

Abstract

Face recognition is becoming more prevalent in addressing several social issues, such as enhancing personal security and verifying identity. Facial recognition is a type of biometric technology that is employed with other commonly used biometric applications, like iris recognition, and vein pattern recognition, along with fingerprint recognition. This type of recognition is a process that identifies an individual by analysing specific characteristics of their physical features. Deep Learning (DL) is a subset of machine learning (ML) that specializes in using neural networks to process images and identify patterns. One of its many applications is in face recognition. DL has led to the widespread use of Convolution Neural Network (CNN) grounded facial recognition technology, making it the leading approach in face identification. This investigation’s chief goal is to explore DL for Face Recognition, which involves the use of Artificial Intelligence (AI) - Powered Solutions with Convolutional Neural Networks. A methodical strategy is suggested to optimize the parameters and improve the system's performance. CNNs have similarities with regular neural networks, yet they specifically assume the inputs happe to be images. This allows designers to integrate certain qualities into the architecture. This study offered the construction and gauging of a real-time facial recognition system utilizing CNN Architecture. The offered system and also CNN architecture are assessed by optimizing several parameters of the CNN in order to boost the recognition correctness of the designed system. So, the offered system achieved a maximum recognition correctness of 98.75% when utilising Kaggle databases for inputs and also 98.00% when utilising real-time inputs.