  Fingerprint classification is an effective technique for reducing the
candidate numbers of fingerprints in the stage of matching in automatic
fingerprint identification system (AFIS). In recent years, deep learning is an
emerging technology which has achieved great success in many fields, such as
image processing, natural language processing and so on. In this paper, we only
choose the orientation field as the input feature and adopt a new method
(stacked sparse autoencoders) based on depth neural network for fingerprint
classification. For the four-class problem, we achieve a classification of 93.1
percent using the depth network structure which has three hidden layers (with
1.8% rejection) in the NIST-DB4 database. And then we propose a novel method
using two classification probabilities for fuzzy classification which can
effectively enhance the accuracy of classification. By only adjusting the
probability threshold, we get the accuracy of classification is 96.1% (setting
threshold is 0.85), 97.2% (setting threshold is 0.90) and 98.0% (setting
threshold is 0.95). Using the fuzzy method, we obtain higher accuracy than
other methods.
