The research in biometric recognition using hand shape has been somewhat stagnating in the last decade. Meanwhile, computer vision and machine learning have experienced a paradigm shift with the renaissance of deep learning, which has set the new state-of-the-art in many related fields. Inspired by successful applications of deep learning for other biometric modalities, we propose a novel approach to 3D hand shape recognition from RGB-D data based on geometric deep learning techniques. We show how to train our model on synthetic data and retain the performance on real samples during test time. To evaluate our method, we provide a new dataset NNHand RGB- D of short video sequences and show encouraging performance compared to diverse baselines on the new data, as well as current benchmark dataset HKPolyU. Moreover, the new dataset opens door to many new research directions in hand shape recognition.

Clustered Dynamic Graph CNN for Biometric 3D Hand Shape Recognition / Svoboda, J.; Astolfi, P.; Boscaini, D.; Masci, J.; Bronstein, M.. - (2020), pp. 1-9. ((Intervento presentato al convegno 2020 IEEE/IAPR International Joint Conference on Biometrics, IJCB 2020 tenutosi a usa nel 2020 [10.1109/IJCB48548.2020.9304894].

Clustered Dynamic Graph CNN for Biometric 3D Hand Shape Recognition

Astolfi P.;
2020

Abstract

The research in biometric recognition using hand shape has been somewhat stagnating in the last decade. Meanwhile, computer vision and machine learning have experienced a paradigm shift with the renaissance of deep learning, which has set the new state-of-the-art in many related fields. Inspired by successful applications of deep learning for other biometric modalities, we propose a novel approach to 3D hand shape recognition from RGB-D data based on geometric deep learning techniques. We show how to train our model on synthetic data and retain the performance on real samples during test time. To evaluate our method, we provide a new dataset NNHand RGB- D of short video sequences and show encouraging performance compared to diverse baselines on the new data, as well as current benchmark dataset HKPolyU. Moreover, the new dataset opens door to many new research directions in hand shape recognition.
IJCB 2020 - IEEE/IAPR International Joint Conference on Biometrics
US
Institute of Electrical and Electronics Engineers Inc.
978-1-7281-9186-7
Svoboda, J.; Astolfi, P.; Boscaini, D.; Masci, J.; Bronstein, M.
Clustered Dynamic Graph CNN for Biometric 3D Hand Shape Recognition / Svoboda, J.; Astolfi, P.; Boscaini, D.; Masci, J.; Bronstein, M.. - (2020), pp. 1-9. ((Intervento presentato al convegno 2020 IEEE/IAPR International Joint Conference on Biometrics, IJCB 2020 tenutosi a usa nel 2020 [10.1109/IJCB48548.2020.9304894].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/296078
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