VISION MARK – AUTOMATED SMART ATTENDENCE
Main Article Content
Abstract
Vision Mark is a face recognition–based smart attendance system designed to overcome the limitations
of traditional attendance methods such as manual registers and card-based systems, which are often time-consuming,
error-prone, and vulnerable to proxy attendance. The system is built using advanced computer vision and deep
learning techniques to ensure accuracy, automation, and reliability. Vision Mark operates through two primary
modules: registration and attendance marking. In the registration phase, users’ facial data is captured and processed
using a pre-trained deep learning model to generate unique facial embeddings. These embeddings serve as
distinctive digital representations of each individual and are securely stored in the system database. In the attendance
marking phase, real-time video streams are analyzed to detect and recognize faces. The system compares detected
faces with stored embeddings using similarity measurement techniques to accurately identify individuals. Once a
match is found, attendance is automatically recorded with a timestamp, ensuring precise tracking. To prevent
duplicate entries, the system incorporates date-based constraints, allowing only one attendance record per user per
session or day. This eliminates redundancy and enhances data integrity. Additionally, Vision Mark is designed to
be scalable and adaptable for both academic institutions and organizational environments. It reduces administrative
workload, minimizes human intervention, and enhances overall efficiency. The system can also be extended with
features such as cloud integration, real-time notifications, analytics dashboards, and multi- device support, making
it a robust and future-ready solution for modern attendance management
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