Daniel Riccio

Prof. Daniel Riccio

Department of Electrical and Information Technology Engineering, Università di Napoli Federico II, 80125 Napoli, Italy
orcid

Research Focus & Metrics

Academic Career Length

23Years (2004-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
1 October 2007 in Pattern Recognition Letters
Verified
Journal Article
Closed access
AAAndrea F. Abate
Andrea F. Abate
Michele NappiMichele Nappi
Michele Nappi
Daniel RiccioDaniel Riccio
Daniel Riccio
GSGabriele Sabatino
Gabriele Sabatino

1 May 2015 in Pattern Recognition Letters
Verified
Journal Article
Closed access
MMMaria De Marsico
Maria De Marsico
Michele NappiMichele Nappi
Michele Nappi
Daniel RiccioDaniel Riccio
Daniel Riccio
HWHarry Wechsler
Harry Wechsler

1 December 2014 in Image and Vision Computing
Verified
Journal Article
Closed access
MMMaria De Marsico
Maria De Marsico
CGChiara Galdi
Chiara Galdi
Michele NappiMichele Nappi
Michele Nappi
Daniel RiccioDaniel Riccio
Daniel Riccio

1 April 2015 in Pattern Recognition
Verified
Journal Article
Closed access
VCVirginio Cantoni
Virginio Cantoni
CGChiara Galdi
Chiara Galdi
Michele NappiMichele Nappi
Michele Nappi
Marco PortaMarco Porta
Marco Porta
Daniel RiccioDaniel Riccio
Daniel Riccio

1 March 2012
Verified
Conference Paper
Closed access
MMMaria De Marsico
Maria De Marsico
Michele NappiMichele Nappi
Michele Nappi
Daniel RiccioDaniel Riccio
Daniel Riccio
JDJean-Luc Dugelay
Jean-Luc Dugelay
Face recognition provides many advantages compared with other available biometrics, but it is particularly subject to spoofing. The most accurate methods in literature addressing this problem, rely on the estimation of the three-dimensionality of faces, which heavily increase the whole cost of the system. This paper proposes an effective and efficient solution to problem of face spoofing. Starting from a set of automatically located facial points, we exploit geometric invariants for detecting replay attacks. The presented results demonstrate the effectiveness and efficiency of the proposed indices.

1 December 2016 in Pattern Recognition Letters
Verified
Journal Article
Closed access
CGChiara Galdi
Chiara Galdi
MNMichele Nappi
Michele Nappi
Daniel RiccioDaniel Riccio
Daniel Riccio
HWHarry Wechsler
Harry Wechsler