MUMBAI, India, Aug. 17 -- Intellectual Property India has published a patent application (202641096136 A) filed by Velammal Institute Of Technology; Mr. Sathiyajebasundar; Mr. Retheesh D; Dr. R. Venkadesh; Chelikam Kavya; Jangiti Revathi; Kethireddy Sivani; and Thalluru Susmitha on August 08, 2026, for Dynamic Hand Gesture Recognition For Human Computer Interactions In Atm System.
Inventors include Mr. Sathiyajebasundar; Mr. Retheesh D; Dr. R. Venkadesh; Chelikam Kavya; Jangiti Revathi; Kethireddy Sivani; and Thalluru Susmitha.
The application for the patent was published on August 14, 2026, under issue no. 33/2026.
Abstract: The present invention introduces a real-time dynamic hand gesture recognition system for secure and touchless human–computer interaction in Automated Teller Machine (ATM) systems. The proposed system utilizes computer vision and deep learning techniques to detect, track, and classify dynamic hand gestures captured through an integrated camera module. The system enables users to perform ATM operations such as cash withdrawal, balance inquiry, fund transfer, and transaction confirmation using predefined hand gestures, thereby eliminating the need for physical touch interfaces. Live video streams are captured and processed frame-by-frame using image preprocessing techniques including noise reduction, background subtraction, and hand segmentation. The system employs hand landmark detection and motion tracking algorithms to extract spatial and temporal features. A Convolutional Neural Network (CNN) combined with a Long Short-Term Memory (LSTM) network is utilized to recognize dynamic gesture patterns accurately. Upon successful gesture classification, the corresponding ATM command is executed securely within the transaction module. The system enhances hygiene, accessibility, and security, particularly in public environments. It reduces physical contact, supports users with mobility challenges, and minimizes hardware wear and tear. The proposed model is computationally efficient, platform-independent, and suitable for real-time deployment in banking environments. By integrating gesture recognition, intelligent classification, and secure transaction processing, the system provides a modern, efficient, and user-friendly solution for next-generation ATM interfaces. Keywords: Dynamic Hand Gesture Recognition, Human–Computer Interaction, ATM Automation, CNN-LSTM Model, Computer Vision, Real-Time Gesture Tracking, Touchless Interface.
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