ELECTRONIC DESIGN AUTOMATION - EDA
Deep learning for Biometric Identification using PPG signals
Parole chiave APPRENDIMENTO PROFONDO, BASSO CONSUMO, BIOSEGNALI, EFFICIENZA ENERGETICA, INTELLIGENZA ARTIFICIALE, MICROCONTROLLORI, PPG, RETI NEURALI CONVOLUZIONALI, RETI NEURALI PROFONDE, RICONOSCIMENTO BIOMETRICO, SISTEMI EMBEDDED
Riferimenti DANIELE JAHIER PAGLIARI
Riferimenti esterni Alessio Burrello (Politecnico di Torino)
Gruppi di ricerca DAUIN - GR-06 - ELECTRONIC DESIGN AUTOMATION - EDA, ELECTRONIC DESIGN AUTOMATION - EDA, GR-06 - ELECTRONIC DESIGN AUTOMATION - EDA
Tipo tesi SPERIMENTALE, SVILUPPO SW
Descrizione Biometric identification systems based on wearable devices are developing quickly in recent years However, the involved machine learning algorithms need dedicated training on each subject in order to obtain good accuracy. In particular, biometric recognition based on machine learning and photoplethysmography (PPG) signals are currently based on the recognition of a subject already seen during training.
The goal of this thesis is to develop a model that does not need further training in order to be extended to new subjects, but only a small amount of reference data.
In particular, the candidate will have to train neural networks able to project the data of a new subject, never seen during training, onto a multi-dimensional space, in which the biometric trace is perfectly distinguishable from all other subjects.
The final product of the thesis will be the validation of the proposed method on public datasets, and the implementation of the complete application on a wearable device.
Interested candidates must send an email to firstname.lastname@example.org attaching their CV and exams' transcript with scores.
Conoscenze richieste Required skills include C and Python programming, and a minimal familiarity with machine/deep learning concepts and the corresponding models.
Note Thesis in collaboration with Prof. Luca Beniniís research group at the University of Bologna and ETH Zurich. The thesis can be carried out either in Torino or in one of the other two universities.
Scadenza validita proposta 31/12/2023 PROPONI LA TUA CANDIDATURA