Person identification using text and image data

David S. Bolme; J. Ross Beveridge; Adele E. Howe

doi:10.1109/BTAS.2007.4401934

Person identification using text and image data

David S. Bolme, J. Ross Beveridge, Adele E. Howe

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

This paper presents a bimodal identification system using text based term vectors and EBGM face recognition. Identification was tested on a database of 118 celebrities downloaded from the internet. The dataset contained multiple images and two biographies for each person. Text based identification had a 100% identification rate for the full biographies. When the text data was artificially restricted to six sentences per subject, rank one identification rates were similar to face recognition (approx. 22%). In this restricted case, combining text identification and face identification showed a significant improvement in the identification rate over either method alone.

Original language	English
Title of host publication	IEEE Conference on Biometrics
Subtitle of host publication	Theory, Applications and Systems, BTAS'07
DOIs	https://doi.org/10.1109/BTAS.2007.4401934
State	Published - 2007
Externally published	Yes
Event	1st IEEE International Conference on Biometrics: Theory, Applications, and Systems, BTAS '07 - Crystal City, VA, United States Duration: Sep 27 2007 → Sep 29 2007

Publication series

Name	IEEE Conference on Biometrics: Theory, Applications and Systems, BTAS'07

Conference

Conference	1st IEEE International Conference on Biometrics: Theory, Applications, and Systems, BTAS '07
Country/Territory	United States
City	Crystal City, VA
Period	09/27/07 → 09/29/07

Access to Document

10.1109/BTAS.2007.4401934

Cite this

@inproceedings{5a78cfa20af943adac8038f0f3146b34,

title = "Person identification using text and image data",

abstract = "This paper presents a bimodal identification system using text based term vectors and EBGM face recognition. Identification was tested on a database of 118 celebrities downloaded from the internet. The dataset contained multiple images and two biographies for each person. Text based identification had a 100% identification rate for the full biographies. When the text data was artificially restricted to six sentences per subject, rank one identification rates were similar to face recognition (approx. 22%). In this restricted case, combining text identification and face identification showed a significant improvement in the identification rate over either method alone.",

author = "Bolme, {David S.} and Beveridge, {J. Ross} and Howe, {Adele E.}",

year = "2007",

doi = "10.1109/BTAS.2007.4401934",

language = "English",

isbn = "9781424415977",

series = "IEEE Conference on Biometrics: Theory, Applications and Systems, BTAS'07",

booktitle = "IEEE Conference on Biometrics",

note = "1st IEEE International Conference on Biometrics: Theory, Applications, and Systems, BTAS '07 ; Conference date: 27-09-2007 Through 29-09-2007",

}

Bolme, DS, Beveridge, JR & Howe, AE 2007, Person identification using text and image data. in IEEE Conference on Biometrics: Theory, Applications and Systems, BTAS'07., 4401934, IEEE Conference on Biometrics: Theory, Applications and Systems, BTAS'07, 1st IEEE International Conference on Biometrics: Theory, Applications, and Systems, BTAS '07, Crystal City, VA, United States, 09/27/07. https://doi.org/10.1109/BTAS.2007.4401934

Person identification using text and image data. / Bolme, David S.; Beveridge, J. Ross; Howe, Adele E.
IEEE Conference on Biometrics: Theory, Applications and Systems, BTAS'07. 2007. 4401934 (IEEE Conference on Biometrics: Theory, Applications and Systems, BTAS'07).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Person identification using text and image data

AU - Bolme, David S.

AU - Beveridge, J. Ross

AU - Howe, Adele E.

PY - 2007

Y1 - 2007

N2 - This paper presents a bimodal identification system using text based term vectors and EBGM face recognition. Identification was tested on a database of 118 celebrities downloaded from the internet. The dataset contained multiple images and two biographies for each person. Text based identification had a 100% identification rate for the full biographies. When the text data was artificially restricted to six sentences per subject, rank one identification rates were similar to face recognition (approx. 22%). In this restricted case, combining text identification and face identification showed a significant improvement in the identification rate over either method alone.

AB - This paper presents a bimodal identification system using text based term vectors and EBGM face recognition. Identification was tested on a database of 118 celebrities downloaded from the internet. The dataset contained multiple images and two biographies for each person. Text based identification had a 100% identification rate for the full biographies. When the text data was artificially restricted to six sentences per subject, rank one identification rates were similar to face recognition (approx. 22%). In this restricted case, combining text identification and face identification showed a significant improvement in the identification rate over either method alone.

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U2 - 10.1109/BTAS.2007.4401934

DO - 10.1109/BTAS.2007.4401934

M3 - Conference contribution

AN - SCOPUS:48649083528

SN - 9781424415977

T3 - IEEE Conference on Biometrics: Theory, Applications and Systems, BTAS'07

BT - IEEE Conference on Biometrics

T2 - 1st IEEE International Conference on Biometrics: Theory, Applications, and Systems, BTAS '07

Y2 - 27 September 2007 through 29 September 2007

ER -

Person identification using text and image data

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