# Monocular Facial Presentation-Attack-Detection: Classifying Near-Infrared Reflectance Patterns
## 1 Introduction  
Face recognition (FR) is becoming the go-to authentication technology. It is convenient: by simply looking at a camera, a person can instantly verify their identity. This is made possible through advances in deep learning [1]; algorithms can now discern a face from over 50,000 [2]. This convenient precision is yielding mass adoption, where over 100 million smart-phones alone employ FR [3]. Other industries are also quickly following suit. High-tech offices have begun to offer it as a means of building access [4]. Hotels are using it to greet customers upon entry [5]. Airports have adopted it for passport verification [6]. Even automobiles are now using it to enable drivers to access and start their vehicle [7]. The problem is FR algorithms can be vulnerable to spoofing attacks. Spoofing attacks are where an attacker presents a facsimile of an enrolled user, such as a picture, to gain unauthorized access. These attacks can be trivial to implement. Many people post photos of themselves on social media or professional websites, all the attacker needs to do is \"replay\" this likeness with the medium of their choice. This ease of attack combined with the wide-spread adoption of FR means there is clear value to developing a mass-implementable spoofing defense measure. The National Institute of Standards and Technologies (NIST) helps clarify different types of attack presentations [8]. ISO 30107 shows that most attacks are generated from 2D imagery, cheap and easy to prepare. Examples include picture print-outs, display photos or videos, or creating a mask from paper or fabric. These attacks are potent for their production simplicity (cheap and only need a social-media photo). Attackers can easily fabricate variations of these instruments and expose detection methods vulnerabilities [8]. Relevant samples are shown in Figure 1 (see next page). More complex attacks can be performed using 3D imagery. These include a latex or silica mask, printed-scaffolds and clay mannequins. While these are academically interesting, they are generally unrealistic. They are expensive to fabricate and require precise imaging, where most examples ironically involve the victim's participation in 3D scanning. For these reasons, both NIST and industry standards deem them out of scope [2,8]. This research proposes that near-infrared material spectroscopy can be used for robust facial presentation-attack-detection (PAD, also known as anti-spoofing). Historically, 3D technologies are typically best-in-class for PAD [9]. This can be completed using depth sensing [10] or using multi-frame deep learning networks [11]. The goal is not necessarily to supplant those methods, but address new goals of being monocular and single-frame. These are relevant due to many FR security systems being monocular, embedded systems. Material spectroscopy is a process of identifying objects by shining a controlled light source and then characterizing the reflection [12]. This reflection is a function of the material's geometry and albedo; hence, an estimation of 3D structure can be made from textural features of the reflected light. Live faces are a combination of multiple surfaces with varying radii of curvature. A proof in the methodology shows how this must necessarily generate a distribution of varying frequencies (as a function of radius of curvature). Conversely, the spoofing attacks considered here are either flat that can be modeled as a simple convex surface. The methodology proof shows how this results in a simple low-frequency distribution. These distributions are further biased by the material, where spoofing materials are essentially uniform and very reflective (e.g., paper, fabric, and displays) and live faces are composed of skin, hair, eyes, etc., that vary in reflectivity. The juxtaposition between the human perspective and illuminated near-infrared is shown in Figure 1. While the spoofs can look realistic to the human eye, they appear essentially washed out to the near-infrared perspective.