Interaction with Three-Dimensional Gesture and Character Input in Virtual Reality Recognizing gestures in different directions and improving user input

Hand gesture recognition is a key aspect to make interaction of virtual reality more convenient. A good way to make users’ idea understood by computers including characters input plays an important role in interaction. Current methods of hand gesture and character input are too limited to make full use of powerful capacity that computers have nowadays. In this paper, we propose a natural 3D input method based on stereo cameras as an interface of human and machine. We segment the hand out based on skin-color detection and train a neural network based on Hu moments to recognize valid and invalid gestures defined in our paper. For valid gestures, we implement stereo matching and 3D coordinate calculation and line them up to formulate characters. Our method can robustly recognize 3D gestures in different directions and make users’ input more free compared with traditional ways

Face recognition with variation in pose angle using face graphs

Automatic recognition of human faces is an important and growing field. Several real-world applications have started to rely on the accuracy of computer-based face recognition systems for their own performance in terms of efficiency, safety and reliability. Many algorithms have already been established in terms of frontal face recognition, where the person to be recognized is looking directly at the camera. More recently, methods for non-frontal face recognition have been proposed. These include work related to 3D rigid face models, component-based 3D morphable models, eigenfaces and elastic bunched graph matching (EBGM). This thesis extends recognition algorithm based on EBGM to establish better face recognition across pose variation. Facial features are localized using active shape models and face recognition is based on elastic bunch graph matching. Recognition is performed by comparing feature descriptors based on Gabor wavelets for various orientations and scales, called jets. Two novel recognition schemes, feature weighting and jet-mapping, are proposed for improved performance of the base scheme, and a combination of the two schemes is considered as a further enhancement. The improvements in performance have been evaluated by studying recognition rates on an existing database and comparing the results with the base recognition scheme over which the schemes have been developed. Improvement of up to 20% has been observed for face pose variation as large as 45°