An Improved Gesture Segmentation Method for Gesture Recognition Based on CNN and YCbCr

With the continuous improvement of people’s requirements for interactive experience, gesture recognition is widely used as a basic human-computer interaction. However, due to the environment, light source, cover, and other factors, the diversity and complexity of gestures have a great impact on gesture recognition. In order to enhance the features of gesture recognition, firstly, the hand skin color is filtered through YCbCr color space to separate the gesture region to be recognized, and the Gaussian filter is used to process the noise of gesture edge; secondly, the morphological gray open operation is used to process the gesture data, the watershed algorithm based on marker is used to segment the gesture contour, and the eight-connected filling algorithm is used to enhance the gesture features; finally, the convolution neural network is used to recognize the gesture data set with fast convergence speed. The experimental results show that the proposed method can recognize all kinds of gestures quickly and accurately with an average recognition success rate of 96.46% and does not significantly increase the recognition time.

A Study of Hand Gesture Segmentation Techniques for Sign Languages

This paper presents about an overview on several methods of segmentation techniques for hand gesture recognition. Hand gesture recognition has evolved tremendously in the recent years because of its ability to interact with machine. Mankind tries to incorporate human gestures into modern technologies like touching movement on screen, virtual reality gaming and sign language prediction. This research aims towards employed on hand gesture recognition for sign language interpretation as a human computer interaction application. Sign Language which uses transmits the sign patterns to convey meaning by hand shapes, orientation and movements to fluently express their thoughts with other person and is normally used by the physically challenged people who cannot speak or hear. Automatic Sign Language which requires robust and accurate techniques for identifying hand signs or a sequence of produced gesture to help interpret their correct meaning. Hand segmentation algorithm where segmentation using different hand detection schemes with required morphological processing. There are many methods which can be used to acquire the respective results depending on its advantage.