@InProceedings{8553302,
  author = {A. Sepas-Moghaddam and F. Pereira and P. L. Correia},
  booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
  title = {Ear Presentation Attack Detection: Benchmarking Study with First Lenslet Light Field Database},
  year = {2018},
  pages = {2355-2359},
  abstract = {Ear recognition has received broad attention from the biometric community and its emerging usage in multiple applications is raising new security concerns, with robustness against presentation attacks being a very active field of research. This paper addresses for the first time the ear presentation attack detection problem by developing an exhaustive benchmarking study on the performance of state-of-the-art light field and non-light field based ear presentation attack detection solutions. In this context, this paper also proposes an appropriate ear artefact database captured with a Lytro ILLUM lenslet light field camera, including both 2D and light field contents, using several types of presentation attack instruments, including laptop, tablet and two different mobile phones. Results show very promising performance for two recent light field based presentation attack detection solutions originally proposed for face presentation attack detection.},
  keywords = {biometrics (access control);cameras;ear;face recognition;security of data;visual databases;lenslet light field database;ear recognition;biometric community;security concerns;ear presentation attack detection problem;exhaustive benchmarking study;nonlight field;ear presentation attack detection solutions;Lytro ILLUM lenslet light field camera;light field contents;presentation attack instruments;face presentation attack detection;ear artefact database;Ear;Databases;Two dimensional displays;Benchmark testing;Feature extraction;Cameras;Ear Presentation Attack Detection;Light Field Imaging;Artefact Database;Feature Extraction},
  doi = {10.23919/EUSIPCO.2018.8553302},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570428081.pdf},
}

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L."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["A."],"propositions":[],"lastnames":["Sepas-Moghaddam"],"suffixes":[]},{"firstnames":["F."],"propositions":[],"lastnames":["Pereira"],"suffixes":[]},{"firstnames":["P.","L."],"propositions":[],"lastnames":["Correia"],"suffixes":[]}],"booktitle":"2018 26th European Signal Processing Conference (EUSIPCO)","title":"Ear Presentation Attack Detection: Benchmarking Study with First Lenslet Light Field Database","year":"2018","pages":"2355-2359","abstract":"Ear recognition has received broad attention from the biometric community and its emerging usage in multiple applications is raising new security concerns, with robustness against presentation attacks being a very active field of research. 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Sepas-Moghaddam and F. Pereira and P. L. Correia},\n booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},\n title = {Ear Presentation Attack Detection: Benchmarking Study with First Lenslet Light Field Database},\n year = {2018},\n pages = {2355-2359},\n abstract = {Ear recognition has received broad attention from the biometric community and its emerging usage in multiple applications is raising new security concerns, with robustness against presentation attacks being a very active field of research. This paper addresses for the first time the ear presentation attack detection problem by developing an exhaustive benchmarking study on the performance of state-of-the-art light field and non-light field based ear presentation attack detection solutions. 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