Face Image Retrieval Revisited

D 2015

Face Image Retrieval Revisited

SEDMIDUBSKÝ, Jan; Vladimír MÍČ a Pavel ZEZULA

Základní údaje

Originální název

Face Image Retrieval Revisited

Autoři

SEDMIDUBSKÝ, Jan; Vladimír MÍČ a Pavel ZEZULA

Vydání

Switzerland, Proceedings of 8th International Conference on Similarity Search and Applications (SISAP 2015), LNCS 9371, od s. 204-216, 13 s. 2015

Nakladatel

Springer

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Švýcarsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Impakt faktor

Impact factor: 0.402 v roce 2005

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14330/15:00080525

Organizační jednotka

Fakulta informatiky

ISBN

978-3-319-25086-1

ISSN

Klíčová slova anglicky

face retrieval; face detection; effectiveness; efficiency; face matcher fusion

Štítky

DISA

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 31. 3. 2016 12:01, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

The objective of face retrieval is to efficiently search an image database with detected faces and identify such faces that belong to the same person as a query face. Unlike most related papers, we concentrate on both retrieval effectiveness and efficiency. High retrieval effectiveness is achieved by proposing a new fusion approach which integrates existing state-of-the-art detection as well as matching methods. We further significantly improve a retrieval quality by employing the concept of multi-face queries along with optional relevance feedback. To be able to efficiently process queries on databases with millions of faces, we apply a specialized indexing algorithm. The proposed solutions are compared against four existing open-source and commercial technologies and experimentally evaluated on the standardized FERET dataset and on a real-life dataset of more than one million face images. The retrieval results demonstrate a significant gain in effectiveness and two-orders of magnitude more efficient query processing, with respect to a single technology executed sequentially.

Návaznosti

VG20122015073, projekt VaV

Název: Efektivní vyhledávání v rozsáhlých biometrických datech (Akronym: EFBIO)

Investor: Ministerstvo vnitra ČR, Efektivní vyhledávání v rozsáhlých biometrických datech

Citovat

SEDMIDUBSKÝ, Jan; Vladimír MÍČ a Pavel ZEZULA. Face Image Retrieval Revisited. In G. Amato et al. Proceedings of 8th International Conference on Similarity Search and Applications (SISAP 2015), LNCS 9371. Switzerland: Springer, 2015, s. 204-216. ISBN 978-3-319-25086-1. Dostupné z: https://doi.org/10.1007/978-3-319-25087-8_19.

@inproceedings{1305715,
   author = {Sedmidubský, Jan and Míč, Vladimír and Zezula, Pavel},
   address = {Switzerland},
   booktitle = {Proceedings of 8th International Conference on Similarity Search and Applications (SISAP 2015), LNCS 9371},
   doi = {https://doi.org/10.1007/978-3-319-25087-8_19},
   editor = {G. Amato et al.},
   keywords = {face retrieval; face detection; effectiveness; efficiency; face matcher fusion},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Switzerland},
   isbn = {978-3-319-25086-1},
   pages = {204-216},
   publisher = {Springer},
   title = {Face Image Retrieval Revisited},
   year = {2015}
}

TY  - CONF
ID  - 1305715
AU  - Sedmidubský, Jan - Míč, Vladimír - Zezula, Pavel
PY  - 2015
TI  - Face Image Retrieval Revisited
PB  - Springer
CY  - Switzerland
SN  - 9783319250861
KW  - face retrieval
KW  - face detection
KW  - effectiveness
KW  - efficiency
KW  - face matcher fusion
N2  - The objective of face retrieval is to efficiently search an image database with detected faces and identify such faces that belong to the same person as a query face. Unlike most related papers, we concentrate on both retrieval effectiveness and efficiency. High retrieval effectiveness is achieved by proposing a new fusion approach which integrates existing state-of-the-art detection as well as matching methods. We further significantly improve a retrieval quality by employing the concept of multi-face queries along with optional relevance feedback. To be able to efficiently process queries on databases with millions of faces, we apply a specialized indexing algorithm. The proposed solutions are compared against four existing open-source and commercial technologies and experimentally evaluated on the standardized FERET dataset and on a real-life dataset of more than one million face images. The retrieval results demonstrate a significant gain in effectiveness and two-orders of magnitude more efficient query processing, with respect to a single technology executed sequentially.
ER  -

SEDMIDUBSKÝ, Jan; Vladimír MÍČ a Pavel ZEZULA. Face Image Retrieval Revisited. In G. Amato et al. \textit{Proceedings of 8th International Conference on Similarity Search and Applications (SISAP 2015), LNCS 9371}. Switzerland: Springer, 2015, s.~204-216. ISBN~978-3-319-25086-1. Dostupné z: https://doi.org/10.1007/978-3-319-25087-8\_{}19.

Přehled o publikaci