@article{1447986,
      recid = {1447986},
      author = {Maltoni, Davide, and Maio, Dario, and Jain, Anil K., and  Feng, Jianjiang,},
      title = {Handbook of fingerprint recognition.},
      pages = {1 online resource :},
      note = {Previous edition: 2009.},
      abstract = {With their distinctiveness and stability over time,  fingerprints continue to be the most widely used anatomical  characteristic in systems that automatically recognize a  person's identity. This fully updated third edition  provides in-depth coverage of the state-of-the-art in  fingerprint recognition readers, feature extraction, and  matching algorithms and applications. Deep learning  (resurgence beginning around 2012) has been a game changer  for artificial intelligence and, in particular, computer  vision and biometrics. Performance improvements (both  recognition accuracy and speed) for most biometric  modalities can be attributed to the use of deep neural  networks along with availability of large training sets and  powerful hardware. Fingerprint recognition has also been  approached by deep learning, resulting in effective and  efficient methods for automated recognition and for  learning robust fixed-length representations. However, the  tiny ridge details in fingerprints known as minutiae are  still competitive with the powerful representations learned  by huge neural networks trained on big data. Features &  Benefits: Reflects the progress made in automated  techniques for fingerprint recognition over the past five  decades Reviews the evolution of sensing technology: from  bulky optical devices to in-display readers in smartphones  Dedicates an entire new chapter to latent fingerprint  recognition, which is nowadays feasible in "lights-out"  mode Introduces classical and learning-based techniques for  local orientation extraction, enhancement, and minutiae  detection Provides an updated review of  presentation-attack-detection techniques and their  performance evaluation Discusses the evolution of minutiae  matching from rich local descriptors to Minutiae Cylinder  Code Presents the development of feature-based matching:  from FingerCode to handcrafted textural features to deep  features Reviews fingerprint synthesis, including recent  Generative Adversarial Networks The revised edition of this  must-read reference, written by leading international  researchers, covers all critical aspects of fingerprint  security system design and technology. It is an essential  resource for all security and biometrics professionals,  researchers, practitioners, developers, and systems  administrators, and can serve as an easy-to-read reference  for an undergraduate or graduate course on biometrics.  Davide Maltoni is full professor in the Department of  Computer Science (DISI) at the University of Bologna, where  he also co-directs the Biometrics Systems Laboratory  (BioLab). Dario Maio is full professor in the DISI and a  co-director of the BioLab. Anil K. Jain is university  distinguished professor in the Department of Computer  Science and Engineering at Michigan State University.  Jianjiang Feng is associate professor in the Department of  Automation at Tsinghua University.},
      url = {http://library.usi.edu/record/1447986},
      doi = {https://doi.org/10.1007/978-3-030-83624-5},
}