## 4 Experimental Results
### Comparison Experiment of Unsupervised Models  
In this subsection, the most advanced, currently available unsupervised models are compared. The experimental results are shown in Table 3. The following is a brief introduction to each of the comparative models.  
AE [44] is an unsupervised learning algorithm that learns a compressed representation of the input data and then reconstructs the original data. It consists of two parts: an encoder and a decoder. VAE [45] is a variant of AE. By introducing a regularization term, the encoded latent variables are made to follow a given prior distribution, and then the reconstruction of data samples is achieved by learning the latent distribution of the data. MemAE [46], on the other hand, is another variant of AE. It introduces a memory module to store the effective information of the input images, and in the anomaly detection task, this module is used to store the features of bona fide samples. OCPAD [9] is the first to use AE for fingerprint anti-spoofing. Through the representation of the latent space and the calculation of the image reconstruction loss, it can still achieve a relatively high anti-spoofing accuracy without relying on attack samples. PPAD [20] is a method proposed based on the MemAE model. Through a sparse addressing memory network and representational consistency constraints, it obtains a more compact boundary for distinguishing bona fide and spoof samples. U_OCFR [21] unifies the anti-spoofing task and the segmentation task. By introducing intermediate features during the fingerprint reconstruction process for the anti-spoofing task, it enables both the segmentation task and the anti-spoofing task to achieve good performance.  
Compared with judging the reconstruction distance of fixed latent space feature maps, learning the average data distribution of bona fide samples is relatively stable. Since most spoof samples contain obvious glass plate information and a small amount of fingerprint film information, the pixel values in most areas are relatively small, and the features of the viable epidermis layer are not obvious. Therefore, the image reconstruction error of spoof samples is much smaller than that of bona fide samples. By magnifying the key areas in the image and establishing a Gaussian distribution of bona fide sample data, due to the rich semantic information and high pixel value distribution, the analysis of high-dimensional data becomes more effective.  
In terms of the AUC value, the method proposed in this paper is 0.9688, which exceeds that of the state-of-the-art U_OCFR model. The EER is 0.0668, showing a slight decrease compared with the most novel method, and there is a relatively obvious decrease in \(BPCER_{10}\). Overall, the model designed in this paper has improvements in various key metrics, demonstrating that introducing the anti-spoofing contribution information of fingerprint tissues is beneficial to the improvement of the performance of anti-spoofing tasks. The proposed method exhibits a relatively higher standard deviation in AUC compared to other models. This is primarily due to the more complex spoof score computation strategy employed, which increases the sensitivity of the model to threshold selection. Since AUCmeasures model performance across a range of threshold values, this sensitivity results in greater variability. In contrast, metrics such as EER and BPCER are computed using fixed thresholds--hence, they show much lower standard deviations. Despite this higher variance, our method achieves a higher upper bound of AUC, indicating strong potential. In real-world deployments, performance can be further optimized by carefully calibrating the thresholds.