Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloSFINGE 3D: A novel benchmark for online detection and recognition of heterogeneous hand gestures from 3D fingers' trajectories
Anno di pubblicazione2020
Autore/iA. Caputo, A. Giachetti, F. Giannini, K. Lupinetti, M. Monti, M. Pegoraro, A. Ranieri
Affiliazioni autoriDepartment of Computer Science, University of Verona, Strada le Grazie 15, Verona, Italy, 37134 Institute of Applied Mathematics and Information Technologies Enrico Magenes National Research Council Genoa Branch, Via De Marini 6, Genova, Italy, 16149
Autori CNR e affiliazioni
  • inglese
AbstractIn recent years gesture recognition has become an increasingly interesting topic for both research and industry. While interaction with a device through a gestural interface is a promising idea in several applications especially in the industrial field, some of the issues related to the task are still considered a challenge. In the scientific literature, a relevant amount of work has been recently presented on the problem of detecting and classifying gestures from 3D hands' joints trajectories that can be captured by cheap devices installed on head-mounted displays and desktop computers. The methods proposed so far can achieve very good results on benchmarks requiring the offline supervised classification of segmented gestures of a particular kind but are not usually tested on the more realistic task of finding gestures execution within a continuous hand tracking session. In this paper, we present a novel benchmark, SFINGE 3D, aimed at evaluating online gesture detection and recognition. The dataset is composed of a dictionary of 13 segmented gestures used as a training set and 72 trajectories each containing 3-5 of the 13 gestures, performed in continuous tracking, padded with random hand movements acting as noise. The presented dataset, captured with a head-mounted Leap Motion device, is particularly suitable to evaluate gesture detection methods in a realistic use-case scenario, as it allows the analysis of online detection performance on heterogeneous gestures, characterized by static hand pose, global hand motions, and finger articulation. We exploited SFINGE 3D to compare two different approaches for the online detection and classification, one based on visual rendering and Convolutional Neural Networks and the other based on geometry-based handcrafted features and dissimilarity-based classifiers. We discuss the results, analyzing strengths and weaknesses of the methods, and deriving useful hints for their improvement.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da232
Pagine a242
Pagine totali-
RivistaComputers & graphics
Attiva dal 1975
Editore: Pergamon Press. - New York,
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 0097-8493
Titolo chiave: Computers & graphics
Titolo proprio: Computers & graphics.
Titolo abbreviato: Comput. graph.
Titolo alternativo: Computers and graphics
Numero volume della rivista91
Fascicolo della rivista-
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-85089516341)
  • ISI Web of Science (WOS) (Codice:000577434300019)
Parole chiaveComputers and graphics, Formatting, Guidelines
Link (URL, URI)
Titolo parallelo-
Scadenza embargo-
Data di accettazione27/02/2020
Note/Altre informazioniOnline first: 7 August 2020
Strutture CNR
  • IMATI — Istituto di matematica applicata e tecnologie informatiche "Enrico Magenes"
Moduli/Attività/Sottoprogetti CNR
  • DIT.AD004.101.001 : Studio e sviluppo di strumenti e metodi per l'analisi e la manipolazione di dati 3D in ambiente di realtà immersiva/aumenta o mista
Progetti Europei-
SFINGE 3D: A novel benchmark for online detection and recognition of heterogeneous hand gestures from 3D fingers' trajectories (documento privato )
Descrizione: Articolo in rivista pubblicato
Tipo documento: application/pdf