MUMBAI, India, Sept. 28 -- Intellectual Property India has published a patent application (202541025126 A) filed by T. Sriharish on March 20, 2025, for Face Recognition And Identification System On Nvidia Jetson Xavier Nx.
Inventors include T. Sriharish; Joheesvara K C; Pranesh J; Kavya D; Deeksha Mm; Sujith D; Karthik Raja S; Selva Harish M; Susindran S; and Preethi C.
The application for the patent was published on September 25, 2026, under issue no. 39/2026.
Abstract: Abstract: As face recognition technology becomes increasingly crucial in various sectors, including security, healthcare, and retail, the demand for high-performance, real-time recognition systems continues to grow. Traditional systems often rely on cloud infrastructure, leading to high latency and potential privacy concerns. The NVIDIA Jetson Xavier platform provides a powerful edge-computing solution for real-time face recognition that addresses these challenges. By utilizing Jetson Xavier's GPU and CPU capabilities, the system is capable of running deep learning algorithms locally, ensuring fast processing and high accuracy even in dynamic and complex environment~. This approach reduces the need for external servers, offers improved security by keeping biometric data locally, and provides a scalable, energy-efficient solution for both small and large-scale deployments. Field of the Invention: The present invention relates to face recognition systems, specifically utilizing edge computing with the NVIDIA Jetson Xavier platform. It aims to provide real-time, efficient, and accurate face recognition, addressing key challenges such as latency, scalability, and data security in diverse application areas, including security surveillance, access control, and user authentication systems. Summary of the Invention: The face recognition system using NVJDIA Jetson Xavier is a powerful, energy-efficient solution designed to provide real-time facial detection and identification. Built on deep learning algorithms, the system ensures high accuracy even in challenging environments. The Jetson Xavier's edge computing capabilities enable the system to perform all recognition tasks locally, reducing the need for cloud processing and ensuring faster results with reduced power consumption. This invention significantly improves the scalability, efficiency, and security of face recognition systems, making it ideal for modem applications that demand speed, precision, and privacy. Objective(s) of the Invention: The primary objective of this invention is to create an advanced face recognition system that operates on the NVIDIA Jetson Xavier platform, capable of performing real-time face detection and identification with high accuracy and low latency. The system aims to address current limitations in traditional face recognition systems, including: • Speed and Latency: By utilizing edge computing, the system ensures minimal delay in processing face recognition tasks. • Energy Efficiency: The Jetson Xavier platform is designed to handle resource-intensive operations with lower power consumption compared to traditional servers. • Scalability: The system can be easily scaled to support a large number of devices and cameras without compromising performance. • Data Privacy and Security: By processing data locally on the edge device, the system minimizes the risks of exposing sensitive biometric information . Background of the Invention: Face recognition has become one of the most reliable and widely used biometric technologies for identifying individuals. However, existing systems often face challenges in terms of processing speed, latency, and the need for constant connectivity to cloud-based servers. In addition, privacy concerns related to the storage and transmission of biometric data have led to an increasing demand for edge-computing solutions that can process face recognition locally, reducing security risks and improving efficiency. The invention addresses these issues by using the Nvidia Jetson Xavier platform, which offers the necessary computational power for advanced face recognition algorithms while maintaining energy efficiency and scalability. By leveraging deep learning and AI, the system is able to deliver high-accuracy face recognition in real-time, even in dynamic environments.
Disclaimer: Curated by HT Syndication.
