Face verification is the task of checking if two provided images contain the face of the same person or not. In this work, we propose a fully-convolutional Siamese architecture to tackle this task, achieving state-of-the-art results on three publicly-released datasets, namely Pandora, High-Resolution Range-based Face Database (HRRFaceD), and CurtinFaces. The proposed method takes depth maps as the input, since depth cameras have been proven to be more reliable in different illumination conditions. Thus, the system is able to work even in the case of the total or partial absence of external light sources, which is a key feature for automotive applications. From the algorithmic point of view, we propose a fully-convolutional architecture with a limited number of parameters, capable of dealing with the small amount of depth data available for training and able to run in real time even on a CPU and embedded boards. The experimental results show acceptable accuracy to allow exploitation in real-world applications with in-board cameras. Finally, exploiting the presence of faces occluded by various head garments and extreme head poses available in the Pandora dataset, we successfully test the proposed system also during strong visual occlusions. The excellent results obtained confirm the efficacy of the proposed method.

Driver Face Verification with Depth Maps / Borghi, Guido; Pini, Stefano; Vezzani, Roberto; Cucchiara, Rita. - In: SENSORS. - ISSN 1424-8220. - (2019), pp. 19-3361. [10.3390/s19153361]

Driver Face Verification with Depth Maps

Guido Borghi;Stefano Pini;Roberto Vezzani;Rita Cucchiara
2019

Abstract

Face verification is the task of checking if two provided images contain the face of the same person or not. In this work, we propose a fully-convolutional Siamese architecture to tackle this task, achieving state-of-the-art results on three publicly-released datasets, namely Pandora, High-Resolution Range-based Face Database (HRRFaceD), and CurtinFaces. The proposed method takes depth maps as the input, since depth cameras have been proven to be more reliable in different illumination conditions. Thus, the system is able to work even in the case of the total or partial absence of external light sources, which is a key feature for automotive applications. From the algorithmic point of view, we propose a fully-convolutional architecture with a limited number of parameters, capable of dealing with the small amount of depth data available for training and able to run in real time even on a CPU and embedded boards. The experimental results show acceptable accuracy to allow exploitation in real-world applications with in-board cameras. Finally, exploiting the presence of faces occluded by various head garments and extreme head poses available in the Pandora dataset, we successfully test the proposed system also during strong visual occlusions. The excellent results obtained confirm the efficacy of the proposed method.
19
3361
Driver Face Verification with Depth Maps / Borghi, Guido; Pini, Stefano; Vezzani, Roberto; Cucchiara, Rita. - In: SENSORS. - ISSN 1424-8220. - (2019), pp. 19-3361. [10.3390/s19153361]
Borghi, Guido; Pini, Stefano; Vezzani, Roberto; Cucchiara, Rita
File in questo prodotto:
File Dimensione Formato  
sensors-19-03361.pdf

accesso aperto

Tipologia: Versione dell'editore (versione pubblicata)
Dimensione 3.01 MB
Formato Adobe PDF
3.01 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1179496
Citazioni
  • ???jsp.display-item.citation.pmc??? 1
  • Scopus 10
  • ???jsp.display-item.citation.isi??? 6
social impact