Effects of Preprocessing in Person Identification Using Ear Features

Conference proceedings article


Authors/Editors


Strategic Research Themes


Publication Details

Author listPatmanee J., Kanprachar S., Chamnongthai K.

PublisherTaiwan Association for Aerosol Research

Publication year2021

Start page443

End page447

Number of pages5

ISBN9781665411974

ISSN1680-8584

eISSN2071-1409

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85125198343&doi=10.1109%2fICSEC53205.2021.9684602&partnerID=40&md5=e9958d8093e35e6692acb15b6cacb914

LanguagesEnglish-Great Britain (EN-GB)


View on publisher site


Abstract

Currently, face masking is strongly recommended for people when being outside in order to prevent the COVID-19 spread. However, by doing so, the face area is significantly blocked by the mask, resulting in an ineffective accuracy for face recognition system. To be able to identify a person while wearing a face mask, an alternative system has to be considered. There have been several studies in ear recognition system in which an impressive accuracy is obtained. In this work, ear recognition system with the AMI ear database is studied.The feature in terms of histogram of oriented gradients (HOG) is used, and the support vector machine (SVM) is adopted for classification process. To increase the recognition accuracy, ear images are preprocessed by adjusting the sharpness level. It is found that using the concatenated HOG features from the sharpened RGB and HSV images, a promising average recognition accuracy of 86% and the standard deviation of 2.91% are obtained. © 2021 IEEE.


Keywords

AMI Ear DatabaseEar recognition systemHistogram of Oriented Gradients (HOG)


Last updated on 2024-08-10 at 12:00