# Monocular Facial Presentation-Attack-Detection: Classifying Near-Infrared Reflectance Patterns
## 1 Introduction
This is because the spoof geometry results in low textural variance and the materials are extremely reflective. A mathematical surface-reflectance model is presented to show live faces should have predictably different reflectance distributions from their spoof counterparts. Fundamentally, it is the different in surface complexity and radii of curvature that drive reflectance variance. One of the challenges of this research is the lack of near-infrared liveliness imagery. To address this, a dataset is collected using 30 diverse adults under varied pose, distance, and lighting. This process is repeated with four spoofing attacks inspired from NIST: paper-mask, spandex-mask, customized COVID mask, and display-replay. This results in a rigorous dataset that includes contrastive liveliness sets over several real-world noise factors, a fundamental contribution to the field. This yields approximately to 80,000 distinct frames, one of the paper's fundamental contributions. Figure 1: juxtaposing the human and illuminated near-infrared perspectives. Spoofs have less texture variance and appear brighter to the infrared camera. The proposed material spectroscopy methodology is evaluated on the collected dataset. It is postulated that the spectroscopy process itself is the unique methodology, such that any reasonable texture algorithm should be able to discriminate liveliness with this imaging approach. This hypothesis is validated by benchmarking a panel of 13 deterministic [13; 14; 15; 16] and deep learning [17; 18; 19; 20; 21] algorithms. Experimental results demonstrate excellent performance. The key contribution is the proposed-methodology enables a conventional MobileNetV3-based classifier [17] to outperform state-of-the-art networks selected from famous competitions [19; 20; 21]. This is not to discount the state-of-the-art (which are based off the smaller ResNet18 [22] encoder, but validate the methodology generates robust features. This reduces PAD to simply identifying a quality texture encoder. These research contributions can be summarized as follows:  
* Novel application material spectroscopy towards facial presentation-attack-detection;
* Novel near-infrared surface-reflectance mathematical model for facial-liveliness;
* Novel near-infrared data-collection of 30 diverse participants with NIST guided attacks that vary instrument, pose, distance, and lighting;
* Benchmarking the proposed methodology with 13 texture algorithms, enabling a conventional MobileNetV3 algorithm to achieve state-of-the-art performance. The rest of the article is organized as follows. Section 2 presents related work associated with 2D inspired facsimiles. Section 3 describes the material spectroscopy theory with mathematical model of how live and spoof faces should reflect light. This model is the inspiration for using texture classification methods. Section 4 outlines the experiment design, including data-collection and algorithm implementation details. Section 5 presents the results, verifying the near-infrared reflectance can used for robust PAD. Section 6 discusses the work and outlines future research directions.