Real Time Hand Gesture Recognition Using Leap Motion Controller Based on CNN-SVM Architechture

In rapidly growing field of Artificial Intelligence (AI), Hand Gesture Recognition (HGR) is an important entity. In the real world system it is very challenging to detect and classify Dynamic Hand Gestures (DHG). As there is considerable diversity in gesture performed by individuals and the system should be real time to overcome the delay between performing and classifying the gesture. In this work, we proposed a new approach for efficient HGR using Convolutional Neural Network (CNN) along with Support Vector Machine (SVM) classifier. CNN used to avoid feature extraction and to minimized the number of trained parameters. However, to reduce the error, Error Break Propagation Algorithm (EBPA) is implemented. For the system's validity and robustness SVM optimizer has been used. An overall accuracy of 93 % has achieved on DHG 14/28 dataset.