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Reseach Article

Face Recognition using One-shot Learning

by Nikhil Thakurdesai, Nikita Raut, Anupam Tripathi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 23
Year of Publication: 2018
Authors: Nikhil Thakurdesai, Nikita Raut, Anupam Tripathi
10.5120/ijca2018918032

Nikhil Thakurdesai, Nikita Raut, Anupam Tripathi . Face Recognition using One-shot Learning. International Journal of Computer Applications. 182, 23 ( Oct 2018), 35-39. DOI=10.5120/ijca2018918032

@article{ 10.5120/ijca2018918032,
author = { Nikhil Thakurdesai, Nikita Raut, Anupam Tripathi },
title = { Face Recognition using One-shot Learning },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2018 },
volume = { 182 },
number = { 23 },
month = { Oct },
year = { 2018 },
issn = { 0975-8887 },
pages = { 35-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number23/30077-2018918032/ },
doi = { 10.5120/ijca2018918032 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:12:17.701163+05:30
%A Nikhil Thakurdesai
%A Nikita Raut
%A Anupam Tripathi
%T Face Recognition using One-shot Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 23
%P 35-39
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Face Recognition represents one of the attractive research areas. It has drawn the attention of many researchers due to its varying applications such as security, healthcare, marketing, identity authentication, surveillance etc. In this order, different face recognition algorithms have been proposed however, one algorithm that stands out in the event of limited dataset is one shot learning. “One shot” means learning from a single training item. This paper discusses a way for solving this problem. Neural networks are notorious for requiring extremely large datasets to reach a considerable accuracy. This paper proposes a method to solve this problem for the face recognition domain by bringing down the number of training samples required to just one and still achieving a decent accuracy close to 90%.

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Index Terms

Computer Science
Information Sciences

Keywords

CNN Transfer learning fully connected network.