Abstract
Our work focuses on metric learning between gesture sample signatures using Siamese Neural Networks (SNN), which aims at modeling semantic relations between classes to extract discriminative features. Our contribution is the notion of polar sine which enables a redefinition of the angular problem. Our final proposal improves inertial gesture classification in two challenging test scenarios, with respective average classification rates of 0.934 ± 0.011 and 0.776 ± 0.025.
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CITATION STYLE
Berlemont, S., Lefebvre, G., Duffner, S., & Garcia, C. (2016). Polar sine based siamese neural network for gesture recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9887 LNCS, pp. 406–414). Springer Verlag. https://doi.org/10.1007/978-3-319-44781-0_48
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