Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154230
Title: Online heterogeneous face recognition based on total-error-rate minimization
Authors: Jang, S.I.
Tan, Geok-Choo
Toh, K.A.
Teoh, A. B. J.
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: 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
Journal: IEEE Transactions on Systems, Man, and Cybernetics : Systems
Abstract: 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.
URI: https://hdl.handle.net/10356/154230
ISSN: 2168-2216
DOI: 10.1109/TSMC.2017.2724761
Rights: © 2017 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SPMS Journal Articles

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