Subject independent evaluation of eyebrows as a stand‐alone biometric
Abstract Considering challenges such as occlusions due to face coverings, ocular biometrics have risen as a potential remedy. Visible light ocular modalities use features extracted from in and around the eyes from ‘selfie’ captures for personal identification. A less‐visited ocular neighbourhood is...
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| Format: | Article |
| Language: | English |
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Wiley
2021-09-01
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| Series: | IET Biometrics |
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| Online Access: | https://doi.org/10.1049/bme2.12033 |
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| _version_ | 1832546744004509696 |
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| author | Hoang (Mark) Nguyen Ajita Rattani Reza Derakhshani |
| author_facet | Hoang (Mark) Nguyen Ajita Rattani Reza Derakhshani |
| author_sort | Hoang (Mark) Nguyen |
| collection | DOAJ |
| description | Abstract Considering challenges such as occlusions due to face coverings, ocular biometrics have risen as a potential remedy. Visible light ocular modalities use features extracted from in and around the eyes from ‘selfie’ captures for personal identification. A less‐visited ocular neighbourhood is the eyebrows region. Previous works on eyebrows have been mostly using subject‐specific verification protocols where identities overlap between the training and test sets and do not directly address the issue of doppelgangers and twins. Here, the evaluation of five deep learning models, lightCNN, ResNet, DenseNet, MobileNetV2, and SqueezeNet, for eyebrow‐based user authentication in a subject independent environment across different data sets, lighting conditions, resolutions, and facial expressions is done. Also a challenging simulated identical twins scenario in our training and testing data sets is presented. Our results show a 4.2% equal error rate (EER) and 0.993 area under the curve (AUC) on a subset of the FACES data set (154 subjects) and 6.8% EER, 0.978 AUC on a subset of the VISOB data set (350 subjects). Despite achieving promising accuracy during short‐term evaluations, the proposed methods showed lower accuracy when matching against long‐term data or when facing data captured under varying illumination and facial expressions. |
| format | Article |
| id | doaj-art-86ff3ed1bbcc40cd8f4d0e176e83b131 |
| institution | Kabale University |
| issn | 2047-4938 2047-4946 |
| language | English |
| publishDate | 2021-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj-art-86ff3ed1bbcc40cd8f4d0e176e83b1312025-02-03T06:47:18ZengWileyIET Biometrics2047-49382047-49462021-09-0110553654710.1049/bme2.12033Subject independent evaluation of eyebrows as a stand‐alone biometricHoang (Mark) Nguyen0Ajita Rattani1Reza Derakhshani2Department of Computer Science and Electrical Engineering University of Missouri–Kansas City Kansas City Missouri USADepartment of Electrical Engineering and Computer Science Wichita State University Wichita Kansas USADepartment of Computer Science and Electrical Engineering University of Missouri–Kansas City Kansas City Missouri USAAbstract Considering challenges such as occlusions due to face coverings, ocular biometrics have risen as a potential remedy. Visible light ocular modalities use features extracted from in and around the eyes from ‘selfie’ captures for personal identification. A less‐visited ocular neighbourhood is the eyebrows region. Previous works on eyebrows have been mostly using subject‐specific verification protocols where identities overlap between the training and test sets and do not directly address the issue of doppelgangers and twins. Here, the evaluation of five deep learning models, lightCNN, ResNet, DenseNet, MobileNetV2, and SqueezeNet, for eyebrow‐based user authentication in a subject independent environment across different data sets, lighting conditions, resolutions, and facial expressions is done. Also a challenging simulated identical twins scenario in our training and testing data sets is presented. Our results show a 4.2% equal error rate (EER) and 0.993 area under the curve (AUC) on a subset of the FACES data set (154 subjects) and 6.8% EER, 0.978 AUC on a subset of the VISOB data set (350 subjects). Despite achieving promising accuracy during short‐term evaluations, the proposed methods showed lower accuracy when matching against long‐term data or when facing data captured under varying illumination and facial expressions.https://doi.org/10.1049/bme2.12033biometrics (access control)eyeface recognitionfeature extractionconvolutional neural netsdeep learning (artificial intelligence) |
| spellingShingle | Hoang (Mark) Nguyen Ajita Rattani Reza Derakhshani Subject independent evaluation of eyebrows as a stand‐alone biometric IET Biometrics biometrics (access control) eye face recognition feature extraction convolutional neural nets deep learning (artificial intelligence) |
| title | Subject independent evaluation of eyebrows as a stand‐alone biometric |
| title_full | Subject independent evaluation of eyebrows as a stand‐alone biometric |
| title_fullStr | Subject independent evaluation of eyebrows as a stand‐alone biometric |
| title_full_unstemmed | Subject independent evaluation of eyebrows as a stand‐alone biometric |
| title_short | Subject independent evaluation of eyebrows as a stand‐alone biometric |
| title_sort | subject independent evaluation of eyebrows as a stand alone biometric |
| topic | biometrics (access control) eye face recognition feature extraction convolutional neural nets deep learning (artificial intelligence) |
| url | https://doi.org/10.1049/bme2.12033 |
| work_keys_str_mv | AT hoangmarknguyen subjectindependentevaluationofeyebrowsasastandalonebiometric AT ajitarattani subjectindependentevaluationofeyebrowsasastandalonebiometric AT rezaderakhshani subjectindependentevaluationofeyebrowsasastandalonebiometric |