Voice Controlled EV System and Locking System with Gesture Based Authentication - Mrs A Praveena; Putala Balanarayana; M Ajay; M S HariharaSudhan; P Johndev (International Journal of Scientific Research in Science and Technology, 2025)
The "Voice Controlled EV System and Locking System with Gesture-Based Authentication" introduces an advanced vehicle security and accessibility solution by integrating voice recognition using Mel-Frequency Cepstral Coefficients (MFCC) and gesture-based authentication via computer vision. This dual-layered system eliminates the need for traditional keys, enhancing both security and convenience by requiring specific voice commands and hand gestures for vehicle access and control.The primary objective is to develop a robust framework that ensures only registered users can unlock and operate the vehicle through authenticated voice and gesture inputs. By merging these technologies, the project creates an intuitive interface while improving system security and reliability. This aligns with the growing demand for smarter, connected vehicles, making it a timely innovation in the automotive sector.The methodology combines expertise in electrical engineering, computer science, signal processing, and motor control systems. The voice recognition module leverages MFCC for accurate speech feature extraction, while the gesture authentication system uses image processing and machine learning for reliable hand movement detection. These subsystems are integrated with a microcontroller-driven motor system that executes validated commands.The project follows a systematic development process, including standalone module creation, integration, and real-world testing to ensure efficiency and reliability. By replacing conventional access mechanisms with voice and gesture controls, this innovation enhances user experience, addresses modern security challenges, and sets a new benchmark for intuitive vehicle interaction.
Keywords: Voice Recognition; Gesture-Based Authentication; Mel-Frequency Cepstral Coefficients; Computer Vision; Electric Vehicle; Security; Biometric Authentication; Smart Vehicle Access; Signal Processing; Machine Learning; Human-Vehicle Interaction