Facial Recognition Using Convolutional Neural Network Using Real-Time Data

Abstract

Recent years have seen the rise of facial recognition as a significant technological
advancement, with several applications in the fields including security, surveillance,
authentication systems, and Human-Computer Interface. Numerous sectors have undergone
radical change as a result of their ability to automatically identify and validate people based on
their facial traits, opening new doors for innovation. The main objective of facial recognition
is to create automated systems that can correctly identify and validate people from pictures or
videos. The limitations of traditional methods in capturing complex and discriminative facial
patterns included the reliance on handmade features and shallow learning techniques. However,
facial recognition has made great progress since the introduction of deep learning, more notably
Convolutional Neural Networks (CNNs). CNNs are the perfect tool for capturing fine facial
characteristics because they have demonstrated an amazing capacity for hierarchical
representations that can be directly learned from unprocessed image data. In this paper, the
authors focus on facial recognition using a CNN model, intending to improve the accuracy and
resilience of this crucial technology. The authors have applied a well-built CNN model to
address the challenges of facial recognition. We utilize deep learning to automatically identify
and extract high-level features from facial images, enabling more accurate and reliable
identification. The CNN model's architecture was thoughtfully created to utilize the underlying
spatial links and regional patterns visible in facial data. By utilizing a large number of
convolutional and pooling layers, the model can successfully capture both low-level qualities
like edges and textures and high-level facial traits like facial landmarks and expressions.