Dynamic gesture recognition using PCA with multiscale theory and HMM

Hai Wu; Alistair Sutherland

doi:10.1117/12.441451

21 September 2001 Dynamic gesture recognition using PCA with multiscale theory and HMM

Hai Wu, Alistair Sutherland

Proceedings Volume 4550, Image Extraction, Segmentation, and Recognition; (2001) https://doi.org/10.1117/12.441451
Event: Multispectral Image Processing and Pattern Recognition, 2001, Wuhan, China

Abstract

In this paper, a dynamic gesture recognition system is presented which requires no special hardware other than a Webcam. The system is based on a novel method combining Principal Component Analysis (PCA) with hierarchical multi-scale theory and Discrete Hidden Markov Models (DHMM). We use a hierarchical decision tree based on multiscale theory. Firstly we convolve all members of the training data with a Gaussian kernel, which blurs differences between images and reduces their separation in feature space. This reduces the number of eigenvectors needed to describe the data. A principal component space is computed from the convolved data. We divide the data in this space into two clusters using the k-means algorithm. Then the level of blurring is reduced and PCA is applied to each of the clusters separately. A new principal component space is formed from each cluster. Each of these spaces is then divided into two and the process is repeated. We thus produce a binary tree of principal component spaces where each level of the tree represents a different degree of blurring. The search time is then proportional to the depth of the tree, which makes it possible to search hundreds of gestures in real time. The output of the decision tree is then input into DHMM to recognize temporal information.

Citation Download Citation

Hai Wu and Alistair Sutherland "Dynamic gesture recognition using PCA with multiscale theory and HMM", Proc. SPIE 4550, Image Extraction, Segmentation, and Recognition, (21 September 2001); https://doi.org/10.1117/12.441451

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