The latest generation cars are often equipped with advanced driver assistance systems, usually known as ADAS (Advanced Driver Assistance Systems). These systems are able to assist the car driver by leveraging several levels of automation. Therefore, it is essential to adapt the ADAS technology to the car driver’s identity to personalize the provided assistance services. For these reasons, such car driver profiling algorithms have been developed by the scientific community. The algorithm herein proposed is able to recognize the driver’s identity with an accuracy close to 99% thanks to ad-hoc specific analysis of the driver’s PhotoPlethysmoGraphic (PPG) signal. In order to rightly identify the driver profile, the proposed approach uses a 1D Dilated Temporal Convolutional Neural Network architecture to learn the features of the collected driver’s PPG signal. The proposed deep architecture is able to correlate the specific PPG features with subject identity enabling the car ADAS services associated with the recognized identity. Extensive validation and testing of the developed pipeline confirmed its reliability and effectiveness.

Advanced Temporal Dilated Convolutional Neural Network for a Robust Car Driver Identification / F. Rundo, F. Trenta, R. Leotta, C. Spampinato, V. Piuri, S. Conoci, R. Donida Labati, F. Scotti, S. Battiato (LECTURE NOTES IN COMPUTER SCIENCE). - In: Pattern Recognition : ICPR International Workshops and Challenges / [a cura di] A. Del Bimbo, R. Cucchiara, S. Sclaroff, G.M. Farinella, T. Mei, M. Bertini, H.J. Escalante, R. Vezzani. - [s.l] : Springer, 2021. - ISBN 9783030687922. - pp. 184-199 (( convegno ICPR Workshop 2020 - TC4 Workshop on Mobile and Wearable Biometrics (WMWB) tenutosi a Milano nel 2021 [10.1007/978-3-030-68793-9_13].

Advanced Temporal Dilated Convolutional Neural Network for a Robust Car Driver Identification

V. Piuri;R. Donida Labati;F. Scotti;
2021

Abstract

The latest generation cars are often equipped with advanced driver assistance systems, usually known as ADAS (Advanced Driver Assistance Systems). These systems are able to assist the car driver by leveraging several levels of automation. Therefore, it is essential to adapt the ADAS technology to the car driver’s identity to personalize the provided assistance services. For these reasons, such car driver profiling algorithms have been developed by the scientific community. The algorithm herein proposed is able to recognize the driver’s identity with an accuracy close to 99% thanks to ad-hoc specific analysis of the driver’s PhotoPlethysmoGraphic (PPG) signal. In order to rightly identify the driver profile, the proposed approach uses a 1D Dilated Temporal Convolutional Neural Network architecture to learn the features of the collected driver’s PPG signal. The proposed deep architecture is able to correlate the specific PPG features with subject identity enabling the car ADAS services associated with the recognized identity. Extensive validation and testing of the developed pipeline confirmed its reliability and effectiveness.
ADAS; Deep learning; Automotive
Settore INF/01 - Informatica
ICPR
Book Part (author)
File in questo prodotto:
File Dimensione Formato  
Advanced_Temporal_1D_CNN_for_a_Robust_Car_Driver_Physiological_Identification.pdf

accesso aperto

Tipologia: Post-print, accepted manuscript ecc. (versione accettata dall'editore)
Dimensione 1.44 MB
Formato Adobe PDF
1.44 MB Adobe PDF Visualizza/Apri
Rundo2021_Chapter_AdvancedTemporalDilatedConvolu.pdf

accesso riservato

Tipologia: Publisher's version/PDF
Dimensione 3.56 MB
Formato Adobe PDF
3.56 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/811655
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? ND
social impact