2019/07/31 by Tarang Chugh, Anil K. Jain, Chugh, Tarang +1
Biochemistry, Genetics and Molecular Biology · Computer Science · Medicine · #Biometric Identification and Security #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Ocular and Laser Science Research #cs.CV
paper · pdf · doi:10.48550/arxiv.1908.00102
Fingerprint presentation attack detection, OCT scanner; 9 pages, 8 figures
arxiv created 2019/07/31 · openalex publication_date 2019/07/31 · arxiv updated 2019/08/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Optical coherent tomography (OCT) fingerprint technology provides rich depth information, including internal fingerprint (papillary junction) and sweat (eccrine) glands, in addition to imaging any fake layers (presentation attacks) placed over finger skin. Unlike 2D surface fingerprint scans, additional depth information provided by the cross-sectional OCT depth profile scans are purported to thwart fingerprint presentation attacks. We develop and evaluate a presentation attack detector (PAD) based on deep convolutional neural network (CNN). Input data to CNN are local patches extracted from the cross-sectional OCT depth profile scans captured using THORLabs Telesto series spectral-domain fingerprint reader. The proposed approach achieves a TDR of 99.73% @ FDR of 0.2% on a database of 3,413 bonafide and 357 PA OCT scans, fabricated using 8 different PA materials. By employing a visualization technique, known as CNN-Fixations, we are able to identify the regions in the OCT scan patches that are crucial for fingerprint PAD detection.