International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 08 | Aug 2023
p-ISSN: 2395-0072
www.irjet.net
Face Recognition System using OpenCV Arun Binoy1, Acsahmol Edwin 2, Jayakrishnan K3 , Pranav S4 1-4
BTECH UG Students, Department of Computer Science and Engineering, TOMS college of Engineering
APJ Abdul Kalam Technological University, Kerala, India ---------------------------------------------------------------------***--------------------------------------------------------------------1.1 SCOPE Abstract - The face recognition system using OpenCV is a system which recognizes know faces and store it with time stamp. Then recognize the unknown faces and store it in another dataset with time stamp. The versatile face recognition system utilizing OpenCV, designed to streamline attendance tracking in online classes, meetings, and residential security applications. Leveraging advanced image processing techniques, the system identifies and verifies participants' faces in real-time. Its adaptable nature allows easy customization of recognized individuals, facilitating efficient management of attendance lists. This technology proves beneficial for remote learning scenarios, virtual meetings, and enhancing security protocols in residential settings. By seamlessly integrating with existing platforms, the system offers a user-friendly and efficient solution to automate attendance management, thereby improving overall convenience and security in various contexts.
The suggested system aims to create and deploy a reliable and effective system for facial recognition technology-based automated attendance tracking. In order to effectively detect and identify people in photos or videos, the project will use facial recognition algorithms and techniques, which will replace the need for manual attendance procedures. The scope most likely includes activities like image preprocessing and enhancement, face detection and recognition techniques implementation, facial feature extraction, and creation and management of a face database. Creating an intuitive user interface, connecting the system with current attendance management systems, and assessing the system's performance in terms of accuracy, speed, scalability, and usability are all potential components of the project.
1.2 FACE DETECTION
Key Words: face recognition, OpenCV, dataset, time stamp, real time recognition.
Changes in an object's position in respect to its surroundings are monitored using face detection. One of the most important security elements in recent years is face detection software. It is used to enhance security equipment that has already been installed, such as the motion sensor illumination on indoor and outdoor security cameras. An advanced facial identification system like this might be automated to detect criminals by using CCTV cameras positioned at numerous locations. The goal of the project is to create an automated system that effectively and reliably tracks attendance using facial recognition technology.
1.INTRODUCTION Face recognition is important because it not only allows us to use our faces as keys, but it also allows face recognition systems to read our expressions in real time. Face recognition is rapidly improving as the Internet of Things grows and new gadgets are developed. Everyone's first concern in the current world is security. People are harassed at home, and the antiquated security mechanisms designed to keep people safe have failed. Various electronic devices, such as mobile phones, laptops, and ATMs, use biometric authentication or passcodes, however these can be easily accessed by thieves through any methods, making them insecure. Every face is unique because it can be recognised, which is essential for determining a person's identity. Facial recognition is a novel biometric technology for criminal identification that offers high accuracy with low intrusion. It is a technique that uses facial recognition to automatically identify and validate people in video or image frames. This study describes a face recognition system that incorporates the best face detection, feature extraction, and classification approaches currently in use. MTCNN and Face Net are two advanced deep learning systems that have received widespread acclaim for their sophistication and modernity. Our video streaming layer provides a continuous and stable experience with no abrupt transitions between consecutive frames.
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Fig -1: Face detection
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