{"title":"Cross-illumination Evaluation of Hand Crafted and Deep Features for Fusion of Selfie Face and Ocular Biometrics","authors":"Leena Kondapi, A. Rattani, R. Derakhshani","doi":"10.1109/HST47167.2019.9032976","DOIUrl":null,"url":null,"abstract":"This paper addresses the implementation of a multiunit biometric system. Results are shown for multi-unit classification with VISible light mobile Ocular Biometric (VISOB) dataset using feature descriptors such as Local Binary Patterns (LBP) and Histogram of oriented gradients (HOG). We also evaluate the pre-trained deep learning models such as VGG16, ResNet18, MobileNetV1, MobileNetV2, and LightCNN9. Experimental evaluation on large scale VISOB dataset suggests that feature-level fusion followed by score-level fusion of left ocular region, right ocular region and face region in office light condition, daylight and dims condition has provided Equal Error Rates (EER) of 9.3%, 8.0% and 10.6% respectively. Also, combining the pretrained models using feature fusion decreased the EER even further.","PeriodicalId":293746,"journal":{"name":"2019 IEEE International Symposium on Technologies for Homeland Security (HST)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Symposium on Technologies for Homeland Security (HST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HST47167.2019.9032976","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
This paper addresses the implementation of a multiunit biometric system. Results are shown for multi-unit classification with VISible light mobile Ocular Biometric (VISOB) dataset using feature descriptors such as Local Binary Patterns (LBP) and Histogram of oriented gradients (HOG). We also evaluate the pre-trained deep learning models such as VGG16, ResNet18, MobileNetV1, MobileNetV2, and LightCNN9. Experimental evaluation on large scale VISOB dataset suggests that feature-level fusion followed by score-level fusion of left ocular region, right ocular region and face region in office light condition, daylight and dims condition has provided Equal Error Rates (EER) of 9.3%, 8.0% and 10.6% respectively. Also, combining the pretrained models using feature fusion decreased the EER even further.
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