[6] cut OCT images into image patches and input them into a convolutional binary classification network for in-depth detection to judge the authenticity of samples. [PERSON] et al. [18] verified the superiority of CNN in supervised fingerprint anti-spoofing methods. [PERSON] et al. [19] proposed the ISPAD dual-branch network to detect spoof samples by learning the internal structure and overall features of fingerprints. However, in the existing methods, the samples used for training and testing come from the same data domain, and the detection performance for spoof samples with unknown data distributions remains unknown. It can be seen that these supervised methods rely on the limited attack samples in the training set and still have the problem of insufficient generalization ability when facing unknown attack samples. In contrast, in view of the diversity and unknown nature of spoof samples, unsupervised methods that only train on bona fide samples are more valuable in practical applications. [PERSON] et al. [9] proposed an autoencoder method based on activation map reconstruction. Without any spoof samples for reference, it not only has better generalization ability but also alleviates the data dependence problem caused by the significant differences between different spoof samples. This method can achieve a true positive rate of 96.59% without using spoof fingerprints. [PERSON] et al. [20] found that there is noise interference in OCT scanned images. Therefore, by extracting the frequency information in the OCT spatio-temporal domain, decomposing the images into four different groups of frequency-domain information, and using wavelet decomposition to remove irrelevant interference information, they improved the performance by 81.89%. [PERSON] et al. [21] introduced a prototype reconstruction module into the denoising autoencoder, used the sparse addressing method to find the prototype most similar to the input image in the memory module, and proposed a representation consistency constraint to make the reconstructed features closer to bona fide fingerprints, thereby increasing the reconstruction distance of spoof samples and reducing the error rate by 40.07%. [PERSON] et al. [22] proposed a brand-new unified representation model. By introducing an anti-spoofing task during the fingerprint reconstruction process, the intermediate features are connected and interact across layers, enabling both the reconstruction task and the anti-spoofing method to achieve good performance. However, through the study of attention heat maps, it can be found that for fingerprints, the key information for judging the authenticity of samples is often concentrated in specific areas of the fingerprints. Most existing methods are based on the overall image. Therefore, there is still an overfitting phenomenon in the anti-spoofing task, a large number of sample features need to be annotated, and the reconstruction distance based on images cannot distinguish between bona fide and spoof samples. In view of the diversity and unknown nature of spoof materials, a more robust anti-spoofing method is needed to ensure the security of fingerprint recognition systems. In this paper, a self-built 1310 nm OCT system is used to collect three-dimensional structural information on fingertips, and an unsupervised fingerprint anti-spoofing method that only requires bona fide samples is proposed, which can more accurately judge the authenticity of samples. Subsequently, a CNN is used to conduct ablation comparison experiments on different sub-dermal tissue structures of the fingers, and the contribution of these structures in the fingerprint anti-spoofing task is analyzed, providing strong evidence for the method proposed in this paper.