Abstract
AbstractAs we move further in twenty-first century, usage of artificial intelligence is becoming more critical and wide spread. Also related Hand Gesture Recognition is taking its shape to make technology closer to real world. Hand gestures are associated with lots of information and with rapid improvement on computer’s vision, hand gesture recognition is now widely used in robot control, intelligent furniture, gaming, and other aspects. Now with advanced PC vision calculations and progress in Human Computer Interface, Hand gesture recognition is becoming more significant. There is huge demand on such kind of interfaces which recognize movement of human body and relate it to some significant and useful information. Picture division calculation plays an important role in choosing motion shapes and direction. It is visual non-verbal hand gestures which communicates information. There are methods to recognize this information such as electronic based, glove based, marker based, and more. The paper presents a study of different hand gesture techniques—loading gesture image and perform contour detection and localization. It is also representing optimized feature vector using PCA and CNN. We get 73.49% accuracy with 75 ms computation time using Linear Kernel and 91.09% accuracy with 90 ms computation time using RBF Kernel and for Single Image matching.KeywordsRBF KernelSVMHand GestureCNNHCIEffectivnet
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