Hand Gesture Recognition based on Convolution Neural Network (CNN) and Support Vector Machine (SVM)

Gestural communication is a type of nonverbal communication in which visible body gestures are utilised to communicate vital messages, either in place of speech or in conjunction with it. The problem of gesture division is presented as a first step toward visual hand gesture recognition, i.e., the detection, analysis, and recognition of gestures through real-time hand sequences. Visual hand recognition and motion tracking are quite challenging to solve due to their inconvenient nature. This research seeks to address the issue by determining which classification technique, Convolutional Neural Network (CNN) or Support Vector Machine (SVM), is superior in recognising hand motions. The hand-skeletal was used as the features to represent the hand gestures. Both classification methods utilised the same sample dataset and camera input to achieve a fair comparison. Then, the performance in terms of accuracy and processing time being analysed. The results indicate that the CNN excels in recognising hand gestures with an accuracy of 97.78% compared to the SVM with 96.30%. In terms of processing time to train/process the datasets, SVM has the upper hand by taking 5 minutes and 16 seconds. Meanwhile the CNN used 8 minutes and 24 seconds.