Hand gesture control accuracy through increased epoch and batch size
O.K. AJAYI · Materials research proceedings · 2023
Abstract. The conventional method of mechanism control which requires physical contact is being replaced with remote control, especially with the advent of the Internet of things (IoT). Facial recognition is used to identify and authenticate faces from images or videos. It has many applications, including granting or restricting access to a facility, or secured areas, or preventing unauthorized users, including selective access. The accuracy of such a recognition and control system is very important and depends on how the system was modeled and adopted. In this study, facial images and gesture images were collated and modeled in a python neural network, then optimized using TensorFlow. The algorithm was compiled unto a raspberry pi for testing on a developed automatic gate. An effective method of achieving accuracy using the epoch is presented in this work. Five (0, 5, 10, 15, 20) batch number and epoch respectively were modeled to achieve accuracy for the system. The model was trained with five gestures; fist, palm, thumb up, thumb, and last finger. The gesture recognition accuracy achieved through the epoch was maximum at 99.97 when both batch number and epoch were set to 20. However, when no epoch was set, the accuracy was below 10.2, whereas there was no accuracy below 98% when the epoch was introduced. This depicts the importance of epoch in achieving accuracy in image recognition. It was also discovered that the higher the epoch and the batch number the greater the accuracy, but the processing would require a very high processing unit.