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 \(0.934 \pm 0.011\) and \(0.776 \pm 0.025\).
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Notes
- 1.
Computations are performed on an Intel\(\copyright \) Core™i7-4800MQ processor at 2.70 GHz.
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Berlemont, S., Lefebvre, G., Duffner, S., Garcia, C. (2016). Polar Sine Based Siamese Neural Network for Gesture Recognition. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9887. Springer, Cham. https://doi.org/10.1007/978-3-319-44781-0_48
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