Facial profiles recognition using comparative facial soft biometrics
Facial profiles recognition using comparative facial soft biometrics
This study extends previous advances in soft biometrics and describes to what extent soft biometrics can be used for facial profile recognition. The purpose of this research is to explore human recognition based on facial profiles in a comparative setting based on soft biometrics. Moreover, in this work, we describe and use a ranking system to determine the recognition rate. The Elo rating system is employed to rank subjects by using their face profiles in a comparative setting. The crucial features responsible for providing useful information describing facial profiles have been identified by using relative methods. Experiments based on a subset of the XM2VTSDB database demonstrate a 96% for recognition rate using 33 features over 50 subjects.
Alamri, Malak
6e6c6422-14b2-4aa8-9936-070face0f285
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Alamri, Malak
6e6c6422-14b2-4aa8-9936-070face0f285
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Alamri, Malak and Mahmoodi, Sasan
(2020)
Facial profiles recognition using comparative facial soft biometrics.
Bromme, A., Busch, C., Dantcheva,, A., Raja, A., Rathgeb, C. and Uhl, A.
(eds.)
In Proceedings of 19th Biometrics Special Interest Group Conference (BioSig).
8 pp
.
(In Press)
Record type:
Conference or Workshop Item
(Paper)
Abstract
This study extends previous advances in soft biometrics and describes to what extent soft biometrics can be used for facial profile recognition. The purpose of this research is to explore human recognition based on facial profiles in a comparative setting based on soft biometrics. Moreover, in this work, we describe and use a ranking system to determine the recognition rate. The Elo rating system is employed to rank subjects by using their face profiles in a comparative setting. The crucial features responsible for providing useful information describing facial profiles have been identified by using relative methods. Experiments based on a subset of the XM2VTSDB database demonstrate a 96% for recognition rate using 33 features over 50 subjects.
More information
Accepted/In Press date: 25 August 2020
Identifiers
Local EPrints ID: 443468
URI: http://eprints.soton.ac.uk/id/eprint/443468
PURE UUID: 5fc162ed-6b8f-406a-b0df-5a5aaaec3a8d
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Date deposited: 26 Aug 2020 16:35
Last modified: 17 Mar 2024 05:51
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Contributors
Author:
Malak Alamri
Author:
Sasan Mahmoodi
Editor:
A. Bromme
Editor:
C. Busch
Editor:
A. Dantcheva,
Editor:
A. Raja
Editor:
C. Rathgeb
Editor:
A. Uhl
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