# Monocular Facial Presentation-Attack-Detection: Classifying Near-Infrared Reflectance Patterns
## 5 Experimental Results
In addition to encoder limitations, there are some potential biases introduced in the other state-of-the-art networks. For example, the dual-branch depth network [21] results are generally good but surprisingly the worst of the deep learning. This can be easily rationalized as a consequence of not having depth-maps for training in this dataset. While a pre-trained version is transfer-learned from the competition datasets, this likely is not enough to retain the depth information when fine-tuning the embedding task. Additionally, the spoof cues network [20] is designed to project a spoof perspective and compare it against the original image. Intuitively, the distance from the projection should be greater when there is a live image presented versus spoof. In practice, there is a potential methodology limitation. Because it is always projecting spoofs, it may incorrectly learn generative features when the input is a spoof (e.g., not learning what constitutes a \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Algorithm** & **ACER** & **NPCER** & \multicolumn{4}{c}{**APCER:**} \\  & & **Paper** & **Spandex** & **COVID** & **Display** \\ \hline Det: LBP \(\ddagger\) & 3.6\% & 4.9\% & 6.4\% & 0.2\% & 0.0\% & ND \\ Det: DCT & 9.8\% & 17.6\% & 1.0\% & 5.2\% & 0.0\% & ND \\ Det: RCH & 14.9\% & 23.3\% & 11.5\% & 4.6\% & 0.0\% & ND \\ Det: RFS & 50.1\% & 99.9\% & 0.4\% & 0.1\% & 0.1\% & ND \\ \hline Det 3D-Ensemble: LBP & 1.7\% & 0.9\% & 7.7\% & 0.0\% & 0.0\% & ND \\ Det 3D-Ensemble: DCT & 3.8\% & 3.8\% & 2.2\% & 8.4\% & 0.5\% & ND \\ Det 3D-Ensemble: RCH & 6.7\% & 8.1\% & 10.2\% & 0.4\% & 5.5\% & ND \\ Det 3D-Ensemble: RFS & 24.8\% & 31.0\% & 17.0\% & 17.0\% & 22.0\% & ND \\ \hline DL Base: MobileNetV3 \(\ddagger\) & 0.2\% & 0.2\% & 0.3\% & 0.0\% & 0.1\% & ND \\ DL Base: InceptionNetV3 & 0.3\% & 0.4\% & 0.5\% & 0.0\% & 0.0\% & ND \\ \hline SOTA: Central-Difference Net & 0.5\% & 0.4\% & 1.6\% & 0.0\% & 0.0\% & ND \\ SOTA: Spoof-Cues Net & 1.0\% & 0.5\% & 2.9\% & 0.1\% & 1.4\% & ND \\ SOTA: Dual-Branch Depth Net & 1.3\% & 0.6\% & 3.2\% & 0.1\% & 2.2\% & ND \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Facial presentation–attack–detection experiment results: lab conditions. \(\dagger\) indicates optimal deterministic algorithm. \(\ddagger\) indicates optimal deep learning algorithm. live face). An interesting adaption would be to project both live and spoof perspectives in a multi-task-learning design, then do a contrastive difference with the input. The spectroscopy approach is also robust to exterior sunlight as given in Table 3. Most algorithms perform within statistical noise of the laboratory conditions (with the exception of the random Fourier series). In some cases, the results actually improve. This is believed to be a result of the diffuse sun-load adding constructively with the illuminator. Recall from the proof the ambient light is essentially a zero-frequency term in Equation (5). This helps with general image quality, as the additional light improves the overall exposure, without impacting the texture features. The outdoor environment, however, does add additional noise. This is visualized in the form of degraded gamma and pepper noise in Figure 4. The introduction of low-frequency noise likely interferes with the deep-learning networks to be discern the liveliness texture features, which causes a general drop in performance except for the base InceptionV3 network [18]. Here, the benefits of the sophisticated encoder becomes present, as it is definitively the most accurate network. Still, the base MobileNetV3 network [17] is indicated as the most optimal for its better combination of similar accuracy with notably better efficiency. Perhaps more interesting is the ranked channel histograms remain stable across lighting conditions. This particular descriptor is hypothesized to be the most light sensitive, but the relative distribution remains sufficient for liveliness. These results suggest either the LBP or MobileNetV3 algorithm should be deployed, depending upon the inference platform.