# Monocular Facial Presentation-Attack-Detection: Classifying Near-Infrared Reflectance Patterns
## 4 Presentation-Attack-Detection Experiment
### Dataset Collection  
Dataset liveliness presentations are given in Table 1. This collection is a robust contribution both due to its spectral content and strong variance in attributes. In total, 30 diverse adults are imaged, representing gender (20 males and 10 females), 6 ethnicity groups and ages ranging between 20 and 65. The participants are imaged with live and spoof presentations under the varied perspectives. Imaging is performed with a 5 mega-pixel FLIR Blackfly monochrome camera [57] employing a 940 nm filter with matching illumination.  
The base condition is the participant themselves sitting in the camera lab, looking at the camera and performing several head nodding motions. This is approximately distance ranging from 0.5 to 1.5 m, yaw ranging from \(-\)45 to 45 degrees and pitch ranging from \(-\)15 to 15 degrees. This process is performed in laboratory dark conditions (940 nm illumination only), laboratory light conditions (940 nm illumination with all lab lights turned on), and diffuse outdoor sun conditions (940 nm illumination with sunlight diffused by glass). This process is then repeated for all spoofing attacks: display-replay, paper mask, spandex mask and customized COVID mask. All images are pre-processed with the Retina Face [58]. Face crops are re-scaled to either 225 \(\times\) 225 for deterministic algorithms or appropriate deep learning network input size.  
In total, there are approximately 80,000 unique frames for evaluation (62,000 laboratory and 18,000 exterior). Note that the sun-diffuse condition is a retro-active add-on; all spoofing attacks are presented, but only a subset of the original participants is still available for imaging. Furthermore, the exterior procedure is optimized to capture the attributes with fewer frames. To avoid data bias, the evaluation is first conducted using only laboratory data and then using the full dataset.  
Sample dataset face-crops are shown in Figure 4 (see next-page). This visualization shows relevant samples imaged using illuminated near-infrared juxtaposed with their human perspective. While these spoofs look generally realistic to the human eye (which is verified using FaceNet [1] embeddings comparison, not shown for space), there are obvious differences in the near-infrared perspectives. Both in a lab and outdoors, the spoofs have reduced textural variance due to the simplified geometry. These texture effects are enhanced by the material albedo, where the paper and fabric are notably higher than skin (causing these spoofs to generally appear lighter). Furthermore, the display-replay attacks do not even show up to the near-infrared camera (they do not emit in IR [55]). This is viewed as a passive form of anti-spoofing. Note that all evaluation (training and testing) is performed using only the illuminated near-infrared imagery. The human-perspective is shown only to visualize how the spoofs look sufficiently realistic.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Presentation**} & \multirow{2}{*}{**Ambient**} & **Distance** & **Yaw** & **Pitch** \\  & & **(Meters)** & **(deg)** & **(deg)** \\ \hline Live (30) & Dark, Lights, Sun * & [0.5, 1.5] & [\(-\)45, 45] & [\(-\)15, 15] \\ display-replay (30) & Dark, Lights, Sun * & [0.5, 1.5] & [\(-\)45, 45] & [\(-\)15, 15] \\ Paper Mask (30) & Dark, Lights, Sun * & [0.5, 1.5] & [\(-\)45, 45] & [\(-\)15, 15] \\ Spandex Mask (30) & Dark, Lights, Sun * & [0.5, 1.5] & [\(-\)45, 45] & [\(-\)15, 15] \\ Face-Print Covid Mask (30) & Dark, Lights, Sun * & [0.5, 1.5] & [\(-\)45, 45] & [\(-\)15, 15] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experiment liveliness presentation matrix. All participants are imaged under every matrix combination with the exception of sun-diffuse (all spoof with some live, indicated by *).