Facial Recognition Using Convolutional Neural Network Using Real-Time Data - Bharath, Kumara J; Manjula, Sanjay Koti; Nor Idayu, Ahmad Azami (INTI International University, 2024)
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.