Publication:
Realization of a Hybrid Locally Connected Extreme Learning Machine with DeepID for Face Verification

Simple item page
dc.citedby7
dc.contributor.authorWong S.Y.en_US
dc.contributor.authorYap K.S.en_US
dc.contributor.authorZhai Q.en_US
dc.contributor.authorLi X.en_US
dc.contributor.authorid55812054100en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid57205080765en_US
dc.contributor.authorid23100514300en_US
dc.date.accessioned2023-05-29T07:30:14Z
dc.date.available2023-05-29T07:30:14Z
dc.date.issued2019
dc.descriptionBackpropagation algorithms; Computation theory; Deep learning; Face recognition; Iterative methods; Knowledge acquisition; Machine learning; Neural networks; Biological learning; Convolutional neural network; DeepID; Extreme learning machine; Face Verification; Fast implementation; Feature mapping; Labeled faces in the wilds (LFW); Learning algorithmsen_US
dc.description.abstractMost existing state-of-the-art deep learning algorithms discover sophisticated representations in huge datasets using convolutional neural networks (CNNs) that mainly adopt backpropagation (BP) algorithm as the backbone for training the face recognition problems. However, since decades ago, BP has been debated for causing trivial issues such as iterative gradient-descent operation, slow convergence rate, local minima, intensive human intervention, exhaustive computation, time-consuming, and so on. On the other hand, a competitive machine learning algorithm called extreme learning machine (ELM) emerged with extreme fast implementation and simple in theory has overcome the challenges faced by BP. The ELM advocates the convergence of machine learning and biological learning for pervasive learning and intelligence and has been extensively researched in widespread applications. Nonetheless, till date, none of the work of ELM has proved its competency in tackling face verification problem. Hence, in this paper, we are going to probe for the first time the feasibility of ELM-based network in handling the face verification task. We devise and propose a novel and distinguished hybrid local receptive field-based extreme learning machine with DeepID (hereinafter denoted as H-ELM-LRF-DeepID), to discriminate face pairs. The experimental results on the YouTube face database, labeled faces in the wild (LFW), and CelebFaces datasets have shed light upon the feasibility and usefulness of the H-ELM-LRF-DeepID in the face verification task. � 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8725548
dc.identifier.doi10.1109/ACCESS.2019.2919806
dc.identifier.epage70460
dc.identifier.scopus2-s2.0-85067406650
dc.identifier.spage70447
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85067406650&doi=10.1109%2fACCESS.2019.2919806&partnerID=40&md5=99a241ef6c0c4a86a3bf09657a818ba0
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25006
dc.identifier.volume7
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleIEEE Access
dc.titleRealization of a Hybrid Locally Connected Extreme Learning Machine with DeepID for Face Verificationen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS