Online heterogeneous face recognition based on total-error-rate minimization

In this paper, we propose a recursive learning formulation for online heterogeneous face recognition (HFR). The main task is to compare between images which are acquired from different sensing spectrums for identity recognition. Using an extreme learning machine, the proposed recursive formulation s...

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Main Authors: Jang, S.I., Tan, Geok-Choo, Toh, K.A., Teoh, A. B. J.
Other Authors: School of Physical and Mathematical Sciences
Format: Article
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/154230
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1542302021-12-31T13:40:26Z Online heterogeneous face recognition based on total-error-rate minimization Jang, S.I. Tan, Geok-Choo Toh, K.A. Teoh, A. B. J. School of Physical and Mathematical Sciences Engineering::Electrical and electronic engineering Extreme Learning Machine (ELM) Heterogeneous Face Recognition (HFR) In this paper, we propose a recursive learning formulation for online heterogeneous face recognition (HFR). The main task is to compare between images which are acquired from different sensing spectrums for identity recognition. Using an extreme learning machine, the proposed recursive formulation seeks a direct optimization to the classification error goal where the solution converges exactly to the batch mode solution. Due to the nonlinear nature of the classification error objective function, formulation of a recursive solution that converges is an important and nontrivial task. Based on this recursive formulation, an online HFR system is designed. The system is evaluated using two challenging heterogeneous face databases with images captured under visible, near infrared and infrared spectrums. The proposed system shows promising performance which is comparable with that of competing state-of-the-arts. . This work was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology under Grant NRF-2015R1D1A1A09061316. This paper was recommended by Associate Editor D. Zhang. 2021-12-16T04:15:09Z 2021-12-16T04:15:09Z 2020 Journal Article Jang, S., Tan, G., Toh, K. & Teoh, A. B. J. (2020). Online heterogeneous face recognition based on total-error-rate minimization. IEEE Transactions On Systems, Man, and Cybernetics : Systems, 50(4), 1286-1299. https://dx.doi.org/10.1109/TSMC.2017.2724761 2168-2216 https://hdl.handle.net/10356/154230 10.1109/TSMC.2017.2724761 2-s2.0-85030671556 4 50 1286 1299 en IEEE Transactions on Systems, Man, and Cybernetics : Systems © 2017 IEEE. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Extreme Learning Machine (ELM)
Heterogeneous Face Recognition (HFR)
spellingShingle Engineering::Electrical and electronic engineering
Extreme Learning Machine (ELM)
Heterogeneous Face Recognition (HFR)
Jang, S.I.
Tan, Geok-Choo
Toh, K.A.
Teoh, A. B. J.
Online heterogeneous face recognition based on total-error-rate minimization
description In this paper, we propose a recursive learning formulation for online heterogeneous face recognition (HFR). The main task is to compare between images which are acquired from different sensing spectrums for identity recognition. Using an extreme learning machine, the proposed recursive formulation seeks a direct optimization to the classification error goal where the solution converges exactly to the batch mode solution. Due to the nonlinear nature of the classification error objective function, formulation of a recursive solution that converges is an important and nontrivial task. Based on this recursive formulation, an online HFR system is designed. The system is evaluated using two challenging heterogeneous face databases with images captured under visible, near infrared and infrared spectrums. The proposed system shows promising performance which is comparable with that of competing state-of-the-arts.
author2 School of Physical and Mathematical Sciences
author_facet School of Physical and Mathematical Sciences
Jang, S.I.
Tan, Geok-Choo
Toh, K.A.
Teoh, A. B. J.
format Article
author Jang, S.I.
Tan, Geok-Choo
Toh, K.A.
Teoh, A. B. J.
author_sort Jang, S.I.
title Online heterogeneous face recognition based on total-error-rate minimization
title_short Online heterogeneous face recognition based on total-error-rate minimization
title_full Online heterogeneous face recognition based on total-error-rate minimization
title_fullStr Online heterogeneous face recognition based on total-error-rate minimization
title_full_unstemmed Online heterogeneous face recognition based on total-error-rate minimization
title_sort online heterogeneous face recognition based on total-error-rate minimization
publishDate 2021
url https://hdl.handle.net/10356/154230
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