Template-Type: ReDIF-Article 1.0
Author-Name: Mallavarapu Manaswini
Author-Email: 2200030098cseh@gmail.com
Author-Name: Patibandla Mahitha 
Author-Email: 2200030133cseh@gmail.com
Author-Name: Shaik Sharmila
Author-Email: 2200030218cseh@gmail.com
Author-Name: D Mythrayee
Author-Email: mythra2325@gmail.com
Title: Cogniface: innovative identity insight through convolutional neural networks
Abstract: Cogniface is an innovative web-based facial recognition system that analyzes real-time facial inputs through a live webcam, matching them against a pre-existing database of faces. It utilizes Convolutional Neural Networks (CNNs) to capture intricate facial features and carry out identity verification. When a face is detected, the system checks if that face is in its database, and if it finds a match, it displays the individual's details along with the system's confidence level. If the face isn't recognized, it simply shows a message stating that the face is unrecognized. By integrating machine learning models like CNNs, the system boosts the accuracy of face recognition. The model's effectiveness is assessed using accuracy metrics, ensuring dependable identity recognition and insights. This application provides cutting-edge real-time facial analysis, making it a strong solution for identity verification.
Keywords: Facial recognition, convolutional neural networks, identity verification, real-time analysis, machine learning
Journal: Edu - Tech Enterprise
Pages: 22
Volume: 3
Issue: 
Year: 2025
Subtitle : 
File-URL: https://ete.sciten.org/index.php/ete/article/view/22/89
File-Format: application/pdf
Handle: RePEc:cua:edutec:v:3:y:2025:i::p:22:id:22