Publication: Realization of a Hybrid Locally Connected Extreme Learning Machine with DeepID for Face Verification
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Date
2019
Authors
Wong S.Y.
Yap K.S.
Zhai Q.
Li X.
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
Most 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.
Description
Backpropagation 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 algorithms