{"title":"Border Control by Multi-biometric Identification using Face and Ear images","authors":"Susara S. Thenuwara, C. Premachandra, H. Kawanaka","doi":"10.1109/ICIPRob54042.2022.9798746","DOIUrl":null,"url":null,"abstract":"Biometrics are critical authorization method in border control areas such as airports. This study explores the usage of the ear and face biometric for verification at the physical appearance of the border points and indicates experimental results collected on a newly made database containing four hundred and twenty images. The images have been taken through a quality module for the purpose of reducing the False Rejection Rate. The approach that was used is The Principal Component Analysis (PCA) that is “eigen ear” for obtaining the recognition rate of 89.3%. After the ear was fused with face biometric, there was an improvement in the recognition. The fusion is done at the level of decision making, hitting a recognition of 97.1%, which is an improvement of 8.2%.","PeriodicalId":435575,"journal":{"name":"2022 2nd International Conference on Image Processing and Robotics (ICIPRob)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 2nd International Conference on Image Processing and Robotics (ICIPRob)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIPRob54042.2022.9798746","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Biometrics are critical authorization method in border control areas such as airports. This study explores the usage of the ear and face biometric for verification at the physical appearance of the border points and indicates experimental results collected on a newly made database containing four hundred and twenty images. The images have been taken through a quality module for the purpose of reducing the False Rejection Rate. The approach that was used is The Principal Component Analysis (PCA) that is “eigen ear” for obtaining the recognition rate of 89.3%. After the ear was fused with face biometric, there was an improvement in the recognition. The fusion is done at the level of decision making, hitting a recognition of 97.1%, which is an improvement of 8.2%.