Gender Prediction Using Iris Features
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-5482-5_50
Reference18 articles.
1. Bobeldyk D, Ross A (2016) Iris or periocular? Exploring sex prediction from near infrared ocular images. Lecture notes in informatics (LNI). In: Proceedings—series of the Gesellschaft Fur Informatik (GI), p 260. https://doi.org/10.1109/BIOSIG.2016.7736928
2. Bobeldyk D, Ross A (2018a) Predicting eye color from near infrared iris images. In: Proceedings—2018 international conference on biometrics, ICB 2018, pp 104–110. https://doi.org/10.1109/ICB2018.2018.00026
3. Bobeldyk D, Ross A (2018b) Analyzing covariate influence on gender and race prediction from near-infrared ocular images. https://doi.org/10.1109/ACCESS.2018.2886275
4. Bobeldyk D, Ross A (2019) Predicting soft biometric attributes from 30 pixels: a case study in NIR ocular images. In: Proceedings—2019 IEEE winter conference on applications of computer vision workshops, WACVW 2019, pp 116–124. https://doi.org/10.1109/WACVW.2019.00024
5. Dantcheva A, Elia P, Ross A (2016) What else does your biometric data reveal? A survey on soft biometrics. IEEE Trans Inf Forensics Secur 11(3):441–467. https://doi.org/10.1109/TIFS.2015.2480381
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1. Impact of Occlusion Masks on Gender Classification from Iris Texture;IET Biometrics;2024-01-27
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