); [EMAIL_ADDRESS] (H.F)  
Zhanqing Li  
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China; [EMAIL_ADDRESS] (Z.L. ); [EMAIL_ADDRESS] (J.J.); [EMAIL_ADDRESS] (J.Q. ); [EMAIL_ADDRESS] (H.F)  
Zhanqing Li  
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China; [EMAIL_ADDRESS] (Z.L. ); [EMAIL_ADDRESS] (J.J.); [EMAIL_ADDRESS] (J.Q. ); [EMAIL_ADDRESS] (H.F)  
Zhanqing Li  
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China; [EMAIL_ADDRESS] (Z.L. ); [EMAIL_ADDRESS] (J.J.); [EMAIL_ADDRESS] (J.Q. ); [EMAIL_ADDRESS] (H.F)  
Zhanqing Li  
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China; [EMAIL_ADDRESS] (Z.L. ); [EMAIL_ADDRESS] (J.J.); [EMAIL_ADDRESS] (J.Q. ); [EMAIL_ADDRESS] (H.F)  
Zhanqing Li  
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China; [EMAIL_ADDRESS] (Z.L. ); [EMAIL_ADDRESS] (J.J.); [EMAIL_ADDRESS] (J.Q. ); [EMAIL_ADDRESS] (H.F)  
Zhanqing Li  
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China; [EMAIL_ADDRESS] (Z.L. ); [EMAIL_ADDRESS] (J.J.); [EMAIL_ADDRESS] (J.Q. ); [EMAIL_ADDRESS] (H.F)  
Zhanqing Li  
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China; [EMAIL_ADDRESS] (Z.L. ); [EMAIL_ADDRESS] (J.J.); [EMAIL_ADDRESS] (J.Q. ); [EMAIL_ADDRESS] (H.F)  
Zhanqing Li  
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China; [EMAIL_ADDRESS] (Z.L. ); [EMAIL_ADDRESS] (J.J.); [EMAIL_ADDRESS] (J.Q. ); [EMAIL_ADDRESS] (H.F)  
Zhanqing Li  
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China; [EMAIL_ADDRESS] (Z.L. ); [EMAIL_ADDRESS] (J.J.); [EMAIL_ADDRESS] (J.Q. ); [EMAIL_ADDRESS] (H.F)  
Zhanqing Li  
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China;within the skin. The 3D fingerprint dataset obtained through OCT technology provides a solid data foundation for the development of more efficient and accurate fingerprint anti-spoofing methods. Due to the diversity of materials used to make spoof samples, anti-spoofing methods use zero-shot [8] or one-class sample [9; 10; 11] approaches to construct deep autoencoders. Only training on bona fide fingerprints can prevent attacks from unknown spoof materials. Currently, OCT fingerprint anti-spoofing methods can be divided into feature-based and learning-based methods. In the early days, for bona fide and spoof samples, the method of manually annotating image features was usually chosen for anti-spoofing tasks. The annotated features include sweat pores [12], physiological features [13], etc. [PERSON] et al. [7] carried out anti-spoofing by judging the presence or absence of the papillary layer. [PERSON] et al. [14] used sweat gland annotations to distinguish spoof samples. [PERSON] et al. [15] used the difference in the deformation amount between internal and external fingerprints to mark bona fide fingerprints. [PERSON] et al. [16] proposed an anti-spoofing method based on one-class support vector machines. [PERSON] et al. first proposed a fully automatic detection method based on highly separable double-layer features, which detected spoof fingerprints with 100% accuracy [17]. The above methods are based on manual feature selection, meaning they require a large amount of work and are inefficient when dealing with large-scale datasets. Learning-based methods can automatically extract the features of input samples, significantly improving the efficiency and generalization ability of anti-spoofing methods. They are mainly divided into two categories: supervised and unsupervised learning. Supervised learning methods learn relevant features through the labels of bona fide and spoof samples for judgment. [PERSON] et al.