Alex Golts, Daniel Khapun, et al.
MICCAI 2021
Fingerprint classification is an important indexing method for any large scale fingerprint recognition system or database as a method for reducing the number of fingerprints that need to be searched when looking for a matching print. Fingerprints are generally classified into broad categories based on global characteristics. This paper describes novel methods of classification using hidden Markov models (HMMs) and decision trees to recognize the ridge structure of the print, without needing to detect singular points. The methods are compared and combined with a standard fingerprint classification algorithm and results for the combination are presented using a standard database of fingerprint images. The paper also describes a method for achieving any level of accuracy required of the system by sacrificing the efficiency of the classifier. The accuracy of the combination classifier is shown to be higher than that of two state-of-the-art systems tested under the same conditions.
Alex Golts, Daniel Khapun, et al.
MICCAI 2021
Aakash Khochare, Yogesh Simmhan, et al.
eScience 2022
Yuta Tsuboi, Yuya Unno, et al.
AAAI 2011
Chen-chia Chang, Wan-hsuan Lin, et al.
ICML 2025