liu.seSearch for publications in DiVA
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Fast facial expression recognition using local binary features and shallow neural networks
Univ Zagreb, Croatia.
Univ Zagreb, Croatia.
Univ Zagreb, Croatia.
Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.ORCID-id: 0000-0002-6763-5487
2020 (engelsk)Inngår i: The Visual Computer, ISSN 0178-2789, E-ISSN 1432-2315, Vol. 36, nr 1, s. 97-112Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Facial expression recognition applications demand accurate and fast algorithms that can run in real time on platforms with limited computational resources. We propose an algorithm that bridges the gap between precise but slow methods and fast but less precise methods. The algorithm combines gentle boost decision trees and neural networks. The gentle boost decision trees are trained to extract highly discriminative feature vectors (local binary features) for each basic facial expression around distinct facial landmark points. These sparse binary features are concatenated and used to jointly optimize facial expression recognition through a shallow neural network architecture. The joint optimization improves the recognition rates of difficult expressions such as fear and sadness. Furthermore, extensive experiments in both within- and cross-database scenarios have been conducted on relevant benchmark data sets for facial expression recognition: CK+, MMI, JAFFE, and SFEW 2.0. The proposed method (LBF-NN) compares favorably with state-of-the-art algorithms while achieving an order of magnitude improvement in execution time.

sted, utgiver, år, opplag, sider
SPRINGER , 2020. Vol. 36, nr 1, s. 97-112
Emneord [en]
Facial expression recognition; Neural networks; Decision tree ensembles; Local binary features
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-164181DOI: 10.1007/s00371-018-1585-8ISI: 000511966800009OAI: oai:DiVA.org:liu-164181DiVA, id: diva2:1413990
Tilgjengelig fra: 2020-03-11 Laget: 2020-03-11 Sist oppdatert: 2020-06-02

Open Access i DiVA

fulltext(953 kB)5 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 953 kBChecksum SHA-512
77d6948fd5619e1346717c2a433c85fc4a3ca878e5dd8d5299503f2e98a24d7027aaace93061e7dde2d9d0edf642285d9058123e76105962b0639c04e94aa827
Type fulltextMimetype application/pdf

Andre lenker

Forlagets fulltekst

Søk i DiVA

Av forfatter/redaktør
Ahlberg, Jörgen
Av organisasjonen
I samme tidsskrift
The Visual Computer

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 5 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 29 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf