# Monocular Facial Presentation-Attack-Detection: Classifying Near-Infrared Reflectance Patterns
## 6 Discussion  
This paper demonstrates that near-infrared reflectance patterns can enable robust monocular facial presentation-attack-detection (PAD). PAD is a key part to securing face recognition, mitigating spoofing attacks without falsely rejecting authorized users. Common spoofing attacks include printed pictures, displaying photos or videos, and fabric masks. State-of-the-art methods historically use 3D features to detect these attacks; however, those methods require either extra depth sensors or complicated multi-frame algorithms. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Algorithm** & **ACER** & **NPCER** & \multicolumn{4}{c}{**APCER:**} \\  & & **Paper** & **Spandex** & **COVID** & **Display** \\ \hline Det: LBP \(\dagger\) & 2.6\% & 4.0\% & 3.5\% & 0.1\% & 0.1\% & ND \\ Det: DCT & 7.9\% & 13.5\% & 1.2\% & 5.6\% & 0.0\% & ND \\ Det: RCH & 9.4\% & 15.9\% & 8.3\% & 0.0\% & 0.1\% & ND \\ Det: RFS & 49.7\% & 7.5\% & 91.4\% & 96.8\% & 87.6\% & ND \\ \hline Det 3D-Ensemble: LBP & 1.6\% & 2.4\% & 1.9\% & 0.2\% & 0.1\% & ND \\ Det 3D-Ensemble: DCT & 4.7\% & 2.9\% & 5.5\% & 13.3\% & 0.4\% & ND \\ Det 3D-Ensemble: RCH & 6.7\% & 10.6 \% & 8.0\% & 0.4\% & 0.3\% & ND \\ Det 3D-Ensemble: RFS & 50.0\% & 99.9\% & 0.1\% & 0.2\% & 0.1\% & ND \\ \hline DL Base: MobileNetV3 \(\ddagger\) & 0.8\% & 1.3\% & 0.3\% & 0.0\% & 0.2\% & ND \\ DL Base: InceptionNetV3 & 0.3\% & 0.4\% & 0.5\% & 0.0\% & 0.0\% & ND \\ \hline SOTA: Central-Difference Net & 1.1\% & 1.8\% & 1.3\% & 0.0\% & 0.0\% & ND \\ SOTA: Spoof-Cues Net & 1.5\% & 1.6\% & 2.2\% & 0.1\% & 1.3\% & ND \\ SOTA: Dual-Branch Depth Net & 1.9\% & 2.0\% & 2.8\% & 0.0\% & 1.9\% & ND \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Facial presentation–attack–detection experiment results: lab and exterior sun conditions. \(\dagger\) indicates optimal deterministic algorithm. \(\ddagger\) indicates optimal deep learning algorithm. This research builds off the theory that 3D-geometry features are useful and proposes a novel way to utilize them in a monocular, single-frame fashion. The contribution is the proposed material spectroscopy mathematical model and demonstration of state-of-the-art performance. Live and spoof faces have predictably different reflectance patterns when illuminated with near-infrared light. This hypothesis is modeled mathematically, where surface reflectance is modeled as a function of geometry and albedo. This reflectance behavior implies the frequency composition is sufficient to discern the facial surface, and, therefore, discern liveliness. This hypothesis is then validated by benchmarking a panel of 13 texture classifiers on the prepared dataset. This dataset is extremely diverse, introducing multiple attack vectors over varied light and position for a total of 80,000 unique frames. As anticipated, most of the algorithms classify liveliness extremely well. Both deterministic and deep learning algorithms achieve quality results. The key finding is this material spectroscopy process enables a traditional MobileNetV3 network [17] to achieve 0.8% average classification error rate--outperforming the algorithms selected for their competition performance. This is not to say that the traditional network is superior (as it has a larger encoder than the competition networks), but validate the methodology generates robust features. This simplifies algorithm design while achieving robust, efficient PAD. Future works aim to expand the dataset noise factors and verify features translate across datasets. This collection is designed to verify the proof, meaning the ambient light is structured to be diffuse. Expanding the environment noise factors to include point-sources and shadows would represent challenging real-world scenarios. Additionally, a fundamental claim is this imaging approach inherently generates reliable liveliness features. Cross-dataset evaluation is planned to demonstrate algorithms trained on this dataset perform robustly on other infrared datasets.