# Monocular Facial Presentation-Attack-Detection: Classifying Near-Infrared Reflectance Patterns
## 5 Experimental Results  
The proposed spectroscopy methodology is evaluated by imaging 30 people and their corresponding spoofs under near-infrared illumination. Based upon the surface reflectance modeling, it is hypothesized texture classifiers can robustly characterize facial liveliness based upon geometric and albedo differences. This hypothesis is evaluated here by benchmarking a panel of texture classifiers on the novel spectroscopy dataset, first using laboratory data then including exterior data (see Section 4 for details). The evaluation results presented next validate the hypothesis, demonstrating excellent facial presentation-attack-detection (PAD) accuracy. Laboratory experimental results (dark and lights on) are given in Table 2 (see next-page). The first result column is the overall accuracy on the entire dataset. The second result column is the nominal presentation classification error rate (NPCER, essentially false-rejection rate of live people). The remaining columns shows the attack-presentation classification error rate (APCER, essentially false-acceptance-rate of spoofs). Note that the display-replay attacks are all represented as not-detected (ND). This is because near-infrared cameras do not see the RGB face emitted from the display. This demonstrates that even deterministic texture descriptors can differentiate the liveliness materials. The local binary pattern algorithm (LBP, indicated by the \(\dagger\)) is the optimal solution for its strong standalone accuracy with minimal class bias. The discrete cosine transform and ranked channel histograms similarly show good general robustness; however, it appears the COVID mask class causes some bias towards false-rejections. This bias can be addressed through the use of the 3D ensemble. Note how not only the average precision improves, but the error rates are better distributed. The one poor performer is the random Fourier series. It is essentially an automatic-rejection tool as a single classifier, and still inconsistent in the 3D ensemble. It is postulated that the frequencies selected are outside of the realistic values for surface-angle \(\theta_{s}\). This means that even when using principal component analysis, the random features are potentially for liveliness. The best in class performance is achieved using deep learning. Surprisingly, the best performer is actually the base network with MobileNetV3 encoder [17] (indicated by the \(\ddagger\)). While counter-intuitive, this is hypothesized for a couple of reasons. First, the state-of-the-art networks introduce sophisticated feature-generation methods, tailored to the competition datasets. In this scenario, the material spectroscopy process inherently well separates the liveliness classes. Hence, this imaging approach simplifies the problem to picking the optimal encoder. Note that the competition networks are also built off the smaller ResNet18 encoder [22], which is efficient, but not particularly potent on ImageNet. This enables the base networks (both MobileNetV3 [17] and InceptionV3 [18]) to better discern the attacks by simply having more robust encoders. Delving further into the networks, a few hypotheses can be made. The texture features here are relatively low-dimensional; this is both presented in the mathematical model and observable in the dataset sample images. This suggests that the MobileNetV3 encoder [17] is sufficient due to its simple parameter space, whereas the more powerful InceptionV3 encoder's high dimensional features [18] are not applicable. This explains why the results are essentially tied with the best network (noting there could also be slight over-fitting to high dimensional noise). Furthermore, this aligns well with why the central-difference net [19] is the best of the state-of-the-art performers. While it also is built off ResNet18 [22], the convolutional kernel is designed to improve low-dimensional texture features. This aligns well with the mathematical model and is roughly in line with the base networks.