Similarity results of_Appling both hybrid restricted Boltzmann machine and deep convolution neural networks to low-resolution face image recognition

Supardi, Julian (2023) Similarity results of_Appling both hybrid restricted Boltzmann machine and deep convolution neural networks to low-resolution face image recognition. Turnitin Universitas Sriwijaya.

This is the latest version of this item.

[thumbnail of Appling both hybrid restricted Boltzmann machine and deep convolution neural networks to lowresolution face image recognition] Text (Appling both hybrid restricted Boltzmann machine and deep convolution neural networks to lowresolution face image recognition)
Appling_both_hybrid_restricted_Boltzmann_machine_a (1).pdf

Download (288kB)
[thumbnail of Appling_both_hybrid_RBM_and_DCNNs_to_FR_low_resolu.pdf] Text
Appling_both_hybrid_RBM_and_DCNNs_to_FR_low_resolu.pdf

Download (3MB)

Abstract

D ue to the difficulty of finding the specific features of faces, in computer vision, low-resolution face image recognition is one of the challenging problems and the accuracy of recognition is still quite low. We were trying to solve this problem using deep learning techniques. Two major parts are used for the proposed method; first the restricted Boltzmann machine is used to preprocess the face images, then the deep convolution neural network is used to do classification. The data set was combined from the Georgia Institute of Technology, Aleix Martinez, and Robert Benavente. Based on this combined data, we conducted the training and testing processes. The proposed method is the first method that combines restricted Boltzmann machine and deep convolution neural networks to do low-resolution face image recognition. From the experimental results, compared to existing methods, the proposed method greatly improves the accuracy of recognition. The proposed method is shown in Figure

Item Type: Other
Subjects: #3 Repository of Lecturer Academic Credit Systems (TPAK) > Conference or Workshop
#3 Repository of Lecturer Academic Credit Systems (TPAK) > Articles Access for TPAK (Not Open Sources)
#3 Repository of Lecturer Academic Credit Systems (TPAK) > Results of Ithenticate Plagiarism and Similarity Checker
Divisions: 09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User: Julian Supardi
Date Deposited: 10 May 2023 06:55
Last Modified: 10 May 2023 06:55
URI: http://repository.unsri.ac.id/id/eprint/101441

Available Versions of this Item

Actions (login required)

View Item View Item