Authors: Assistant Professor Mrs. B. Aruna Kumari, Immadi Naga Varshitha, Ramadeni Vasavi, Para Prasanthi, Marripudi Jeevana Jyothi
Abstract: One of the essential technologies that allow implementing the human-computer interaction built intuitively and with a certain level of comfort is the recognition of hand gestures, in particular, in the smart home automation systems. This paper presents a new deep learning model, Attention-Enhanced CNN Gesture Recognition (AE-CNN-GR) that can enhance the quality, responsiveness, and resilience of gesture control on live camera streams and improve the accuracy. The model is based on the extension of the traditional CNN architecture, incorporating channel and spatial attention units, to enable the network to concentrate on the most informative parts of the hand, such as fine finger movements and changes of the positions. Channel attention module records finer spectral and intensity differences in parts of the hands and the spatial attention mechanism focuses on important geometric and contextual characteristics of gestures to enhance the accuracy of classification and boundary detection. The methods of transferMediaPipe and OpenCV identifications and preprocessing using hand detection and appliance control with the use of the Arduino simulation.
