A Comparative Analysis on Hand Gesture Recognition using Deep Learning
Preeti Nutipalli · International Journal for Research in Applied Science and Engineering Technology · 2020
In recent years, the vision-based innovation of hand motion acknowledgment is a significant piece of human computer interaction (HCI).In the last decades; keyboard and mouse play a significant role in human-computer interaction. However, owing to the rapid development of hardware and software, new types of HCI methods have been required. In particular technologies, such as speech recognition and gesture recognition receive great attention in the field of HCI. Hand gesture recognition is very significant for human-computer interaction. On survey many models were used to recognize hand gestures with different custom images from various datasets captured by camera. One of the approaches that highly used in image feature extraction is Convolution neural network (CNN). In this work, we present a novel real-time method for hand gesture recognition. In our framework, the hand gesture is detected from the trained dataset of images. Then, the palm and fingers are segmented so as to detect and recognize the fingers. Finally, a rule classifier is applied to predict the labels of hand gestures. The experiments on the data set of 2569 images show that our method performs well and is highly efficient. Finally we have shown the comparison among the CNN with Softmax and KNN classifier on the images which leads accurate result.