Abstract
This comprehensive survey highlights the state-of-the-art solutions to fingerprint liveness detection across a variety of datasets and scanner models. This chapter includes most algorithms published between 2014 and 2019, which are ranked according to the Average Classification Error (ACE, the average of the statistical Type I and II errors), Error Rate (ER, the ratio of misclassified fingerprints to total fingerprints), or Accuracy Rate (AR, the ratio of correctly classified fingerprints to total fingerprints), for each scanner model in each dataset. Most algorithms surveyed in this chapter were tested on the various LivDet datasets, but other popular datasets such as ATVS and FVC2000 are included in this survey as well. This chapter reviews the LivDet competition series and its progress over time, the various published algorithm performances on all available LivDet datasets (2009–2017), the performance of traditional machine learning algorithms and their variants, and the performance on miscellaneous datasets. This chapter aims to facilitate the research and development of novel liveness classification algorithms through a clear comparison of algorithm performance.
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Kiefer, R., Patel, A. (2021). A Comprehensive Survey on Fingerprint Liveness Detection Algorithms by Database and Scanner Model. In: Daimi, K., Arabnia, H.R., Deligiannidis, L., Hwang, MS., Tinetti, F.G. (eds) Advances in Security, Networks, and Internet of Things. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-71017-0_4
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DOI: https://doi.org/10.1007/978-3-030-71017-0_4
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