Towards automated large vocabulary gesture search
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
A database-based framework for gesture recognition
Personal and Ubiquitous Computing
Bimanual natural user interaction for 3D modelling application using stereo computer vision
Proceedings of the 13th International Conference of the NZ Chapter of the ACM's Special Interest Group on Human-Computer Interaction
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Principle Component Analysis (PCA) and Multiple Discriminant Analysis (MDA) have long been used for the appearance-based hand posture recognition. In this paper, we propose a novel PCA/MDA scheme for hand posture recognition. The scheme is represented by two layers of nodes (classes). The first layer of nodes acts as a crude classification using PCA and each input pattern will be given a likelihood of being in the nodes of this layer. Then MDA is applied locally to the postures in each node of the first layer to give a precise classification of the postures. Each precise class is a node in the second layer. For training, unsupervised classification at the first layer can be obtained using Expectation-Maximization (EM). For better training results, a feedback from each node in the second layer is introduced in the EM process. The experiments on a 100-sign vocabulary show a significant improvement from 57.0% to 63.5%, compared with the global MDA. If combined with HMM for movement modeling, about 93.5% recognition rate is achieved for testing data.