Objective

2

Fingerprint biometric plays a vital role in authenticating a person in a proper way. Fingerprint classification is crucial because it minimizes the search time of a large database

whereas authentication plays a significant role in the fingerprint identification system. Several problems

such as complex operation process

numerous parameters

large-scale data

and inadequate use of the fingerprint information

still prevail.

Method

2

Traditional machine learning methods include supervised

unsupervised

and semi-supervised learning. Most methods assume that the distribution of annotated data is the same as that of unannotated ones. By contrast

transfer learning allows the domains

tasks

and distributions used in training and testing to be different and is highly concerned with target task training. The roles of source and target tasks are no longer symmetric in transfer learning. Transfer learning can not only overcome the drawback of small data scales but also make the model algorithm highly personalized. Therefore

a novel lightweight fingerprint classification model based on transfer learning is presented in this paper. Transfer learning is combined with small target data to improve network generalization further. First

a classical method based on gradient estimation is used to obtain the orientation field

which is divided into three parts

namely

Gaussian smoothing

gradient calculation

and orientation field estimation. The orientation field of the fingerprint image is enhanced to obtain large-scale data. Then

the enhanced image is used as the input of the lightweight Finger-SqueezeNet proposed in this work to obtain effective classification for initially adjusting the parameters. The Finger-SqueezeNet network model is mainly composed of five fire modules and two convolutional layers. The fire module minimizes the network parameters by replacing the 3×3 convolution with 1×1 convolution. However

parallel 3×3 and 1×1 convolutions are combined as the module output to guarantee classification accuracy. Finally

parameters of the pre-trained network model are fine-tuned on the basis of NIST-DB4

whereas some of them in front of the model are retained. Feature and model migrations are mixed for use. The fingerprint can complete the transfer learning at the feature level because the fingerprint orientation field map and fingerprint image belong to different feature expression spaces. Assume that model parameters can be shared in the source and target data. The transfer learning method based on the model is implemented by finding the shared parameter information between the source and target domains. Then

the method is fine-tuned to optimize the model directionally.

Result

2

The average classification accuracy of the proposed pure network model without transfer learning is approximately 93%. The network model with pre-trained transfer learning can reach 98.45%. Moreover

the test result obtained by the single fingerprint validation reaches 95.73%. In general

the classification of whorl fingerprints exhibits a good performance among the five categories. However

the arch and tented arch are relatively poor because of the 17.5% fuzzy labels of these two types of fingerprint. The two types of fuzzy fingerprints are separated from one class to improve the result

and both refused to be recognized

resulting in zero rejection rate. Final results show that the model exhibits strong generalization capability and high stability for fingerprint classification with different qualities. In addition

the presented model can dramatically minimize the parameters with high accuracy.

Conclusion

2

Fingerprint intra-class transfer learning method combined with lightweight neural network can not only fully utilize fingerprint information but also exhibit promising application prospects in mobile terminal.