The Real-time Identification Management System (RIMS) employs an AI-based facial recognition algorithm, which works on deep learning to identify and authenticate individuals by analyzing their facial features. It captures images extracts unique characteristics, and then compares these features with a database of known facial templates, ensuring precise identification. The system leverages deep learning techniques often involving convolutional neural networks (CNNs), to process and learn from extensive facial data, continually enhancing its accuracy.
RIMS is a framework designed to capture, identify, and manage various users (i.e. department staff, students, or social welfare scheme beneficiaries) conveniently through a mobile app, eliminating the need for additional hardware, thus reducing costs. Our product is engineered to adapt to evolving facial features over time and functions seamlessly under all lighting conditions. This adaptability is crucial as human faces can change due to various factors, including aging, facial hair, hairstyle alterations, changing expressions, variations in complexion, the use of eyeglasses and more.
This framework offers a versatile, user-friendly, and cost-effective solution that enhances security, boosts operational efficiency, and improves user experiences across a wide range of applications, from surveillance and access control to authentication and attendance management. It can be seamlessly implemented across various departments / domains. Currently, RIMS is in use for authenticating over 10 million plus facial templates daily across different departments and use cases.
Mobile-based solution that doesn’t require any investment in hardware.
AI algorithms uncover hidden correlations, anomalies, and opportunities within the data, enabling data-driven decision-making.
AI algorithms detect duplicates by analysing facial features and patterns, thus mitigating malpractices and errors.
Self-attendance with location data to enhance experiences, provide valuable insights, and enable context-aware interactions.
Offline capture and tracking of attendance to serve remote areas with network challenges or during events held in temporary settings.
Role-based access to real-time insights and MIS reports generated from health centre data, staff data, administrative tasks, and medical resource allocation.
Verification of presence of a live person, preventing fraudulent attempts to deceive the system using static images or videos.
Advanced algorithms to accurately detect and identify multiple faces simultaneously, even in complex and crowded scenes.
Can be integrated with unified identification mechanisms like Aadhar to filter duplicate enrolments.
Customisable reports and dashboards to enable data-driven decisions, visibility across departments, and optimal resource allocation.
Monitor and digitise attendance in educational institutions and enterprises through a mobile device.
This ground breaking product is playing very critical role in elimination of possible ghost beneficiaries / enrollments aross various domains thereby causing significant OPEX savings to the respective departments.
Elimination of physical devices like biometric machines as mobile camera is used for identity verification.
Minimal risk of human errors caused by fatigue, oversight, or subjectivity.
Unparalleled accuracy in facial recognition at lightning speed.
It is a touchless and designed to work on a mobile device, making it convenient and portable enough to be used anywhere.
Automation of routine tasks and workflows frees up employees to focus on more value-added activities, leading to increased productivity and a better utilisation of human resources.
Automation of identification process and data-driven insights reduce manual effort and errors, thus saving time and administrative costs.
Deployed over of an easy-to-use mobile app that doesn’t require the user to have any advanced technical skills.
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