## 2 Related Work
### Evaluation Metrics for the Anti-Spoofing Performance of OCT Fingerprints  
The evaluation metrics for OCT fingerprint anti-spoofing include the offlowing: the area under the curve (AUC), the equal error rate (EER), and the bona fide presentation classification error rate (BPCER).  
AUC calculates the area under the curve. The curve usually refers to the receiver operator characteristic curve (ROC) or the precision-recall curve (PRC). In this paper, the area under the ROC curve is used. The value of AUC ranges from 0 to 1. The closer it is to 1, the better the model performance. When the AUC value is 1, it means that the model's prediction is completely correct. The ROC curve provides a graphical analysis interface. The abscissa represents the false positive rate (FPR), and the ordinate represents the true positive rate (TPR), which is also called the recall. FPR indicates the proportion of spoof samples that are wrongly judged as bona fide samples among all spoof samples, and TPR indicates the proportion of bona fide samples that are correctly judged as bona fide samples among all bona fide samples. As shown in Figure 5, the shaded part represents the AUC, and the intersection point of the green dotted line and the blue ROC curve represents the value when FPR is equal to TPR. Therefore, the performance of the classification method can be intuitively measured through the ROC curve. The closer the ROC curve is to the upper-left corner, the better the model performance. In this case, the true positive rate is higher and the false positive rate is lower. Therefore, the AUC curve is an important metric for evaluating the performance of binary classification models, which can reflect the model's prediction ability and accuracy.  
The EER is one of the important performance metrics in binary classification problems, especially in the fields of biometric recognition, security verification, etc. Specifically, in the ROC, the EER refers to the error rate corresponding to the point where the FPR is equal to the FNR, that is, when the probabilities are equal. The lower the EER, the better the performance of the classifier, indicating that under the same error rate, the classifier can correctly identify more positive samples. The value range of EER is [0, 1], and the closer it is to 0, the better the performance.  
The BPCER is an evaluation metric for measuring the performance of fingerprint recognition methods. It represents the proportion of bona fide identities that are wrongly rejected, that is, the proportion of cases where the system wrongly identifies a user's bona fide biometric feature as an illegal feature when the user provides a bona fide biometric  
Figure 4: Fingerprint segmentation examples. (**a**) Bona fide fingerprint B-scan image. (**b**) Labeled image. (**c**) Glass layer label. (**d**) Stratum comeum label. (**e**) Viable epidermis label. The colored lines in (**b**) represents the layer surfaces.  
feature. It is usually used in conjunction with the APCER (Attack Presentation Classification Error Rate) to comprehensively evaluate the performance of the system. Corresponding to the APCER, the lower the BPCER, the better, indicating that the proportion of the system wrongly rejecting legitimate users is lower--that is, the system has a higher recognition accuracy for legitimate users.