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Ear recognition system has been widely studied whereas there are just a fewear presentation attack detection methods for ear recognition systems,consequently, there is no publicly available ear presentation attack detection(PAD) database. In this paper, we propose a PAD method using a pre-trained deepneural network and release a new dataset called Warsaw University of TechnologyEar Dataset for Presentation Attack Detection (WUT-Ear V1.0). There is no eardatabase that is captured using mobile devices. Hence, we have captured morethan 8500 genuine ear images from 134 subjects and more than 8500 fake earimages using. We made replay-attack and photo print attacks with 3 differentmobile devices. Our approach achieves 99.83% and 0.08% for the half total errorrate (HTER) and attack presentation classification error rate (APCER),respectively, on the replay-attack database. The captured data is analyzed andvisualized statistically to find out its importance and make it a benchmark forfurther research. The experiments have been found out a secure PAD method forear recognition system, publicly available ear image, and ear PAD dataset. Thecodes and evaluation results are publicly available athttps://github.com/Jalilnkh/KartalOl-EAR-PAD.

Transfer learning using deep neural networks for Ear Presentation Attack Detection: New Database for PAD