Abstract
Computational cost-ineffectiveness and ambiguity due to self-occlusion are bottlenecks of vision-based hand gesture recognition. In this study, the authors address these issues by proposing a novel multi-view hand gesture recognition method based on Pareto optimal front. They first present an oriented gradient local binary pattern operator to generate a groupwise gesture feature data set for multi-view hand gesture images. Then they take hand gesture recognition over multi-view hand images as a multi-query image retrieval problem and Pareto optimal front is constructed based on the dissimilarities between the testing images and sample images. The gesture corresponding to the point with the shortest distance to the origin on the Pareto optimal front is the final recognised result. Extensive experiments verify the accuracy and efficiency of their Pareto optimal font-based method.
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