目的 目前的指纹分类模型存在操作繁琐、参数较多、所需数据规模大、无法充分利用指纹特征信息等问题，而进行快速准确的指纹分类在大型指纹识别系统中至关重要。方法 传统的机器学习方法大多假设已标注数据与未标注数据的分布是相同的，而迁移学习允许源空间、任务空间在测试集和训练集中的分布是不同的，并且迁移学习仅专注目标任务的训练，使得网络模型根据需求更具个性化。因此，本文提出一种基于迁移学习的轻量级指纹分类模型。该模型结合迁移学习，首先采用梯度估计的方法求取指纹图像的方向场图并且做增强处理；然后将扩展的指纹方向场图数据集用于本文提出的轻量级Finger-SqueezeNet的预训练，使其达到一定的分类效果，从而初步实现网络模型参数的调整；最后保留预训练模型部分的网络参数不变，使用指纹图像数据集NIST-DB4对Finger-SqueezeNet网络进行参数微调（fine tuning）。结果 在使用相同的指纹数据集在本文提出的纯网络模型进行分类训练后发现，未采用迁移学习方法对网络模型进行预训练得到的平均分类结果为93%，而通过预训练后的网络模型可以达到98.45%，最终采用单枚指纹测试的方法得到的测试结果达到95.73%。对比同种类型的方法以及验证标准后可知，本文的指纹分类模型在大幅度减少网络参数的同时仍能达到较高的准确率。结论 采用指纹类内迁移学习方法和轻量级神经网络相结合进行分类，适当利用了指纹特征信息，而且有望使指纹分类模型拓展到移动端。

Objective 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 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 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 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.