Static vision based Hand Gesture recognition using principal component analysis

Gesture recognition turns up to be important field in the recent years. Communication through gestures has been used since early ages not only by physically challenged persons hut nowadays for many other applications. Interacting with physical world using expressive body movements is much easier and effective than just speaking As most predominantly hand is used to perform gestures. Hand Gesture Recognition have been widely accepted for numerous applications such as human computer interactions, robotics, sign language recognition, etc Hand Gesture recognition techniques are basically divided into vision based and sensor based techniques. This paper focuses on vision based hand gesture recognition system by proposing a scheme using a database-driven hand gesture recognition based upon skin color model approach and thresholding approach along with an effective template matching using PCA. Initially, hand region is segmented by applying skin color model in YCbCr color space. In the next stage otsuthresholding is applied to separate foreground and background. Finally, template based matching technique is developed using Principal Component Analysis (PCA) for recognition. The system is tested with 4 gestures with 5 different poses per gesture from 4 subjects making 20 images per gesture and shows 91.25% average accuracy and 0.098251 seconds average recognition time and finally confusion matrix is drawn.