# The Impact of Pressure on the Fingerprint Impression: Presentation Attack Detection Scheme
## 2 Related Work
### Previous Studies on Fingerprint Pressure  
The influence of pressure on the fingerprint impression has been attracting researchers from biometrics and forensics fields. [PERSON] et al. carried out an experiment studying the quality of latent fingermarks under pressure [30]. The experiment was conducted by deposing the finger on different surfaces under a range of weights from 0.1 to 10 kg. The study confirmed that the size of the impression increases with increasing pressure while the impression quality varies differently on the different surfaces. Moreover, [PERSON] et al. [31] proposed a fingerprint rectification system that estimates the center and the direction of the fingerprint, then it detects the distorted fingerprint, discovers the distorted pattern, and applies image transformation. Although this method proved a significant enhancement in the speed, the authors revealed some limitations related to the accuracy of pose estimation. From a practical perspective, [PERSON] et al. proposed a capacitive fingerprint sensor array with multiplexed, simultaneous detection of tactile pressure and finger skin temperature for mobile smart devices [32]. The proposed fingerprint sensor array can be integrated with all transparent forms of tactile pressure sensors and skin temperature sensors to enable the detection of a finger pressing on the display. Initial studies in fingerprint PAD using the dynamic distortion followed the conclusions of [PERSON] [33]. A systematic study on skin distortion was conducted to analyze the distortion caused by the elasticity of human skin [34]. Based on the research observations, the experiment initially suggests that genuine fingerprints and PAIs cause different distortions since artificial fingerprints are more rigid, consequently cause lower distortion compared to genuine fingerprints. In their paper, the authors argue that even when a high elastic PAI species is used to attack the system, it is very difficult to precisely emulate the distortion of genuine fingerprints since the behavior is identified with the manner in which the outside skin is anchored to the underlying derr and impacted by the position and state of the finger bone. In order to validate those assumptions, a dynamic dataset was collected using an optical sensor (high frame rate), with user instructions on presenting the fingerprint with rotation and pressure. The evaluation included presentations from bona fide capture subjects and five PAI species (Table 1). For each presentation, the method computes the optical flow, distortion map, and distortion code consecutively, and afterward compares the distortion codes to detect attacks. Different from the latter technique, [PERSON] et al. used a thin-plate spline (TPS) model to globally characterize fingerprint distortion and utilize this model to detect malicious presentations performed by PAI species [35]. The experiment relied on the same assumptions in [33], which state that genuine fingerprints produce a unique distortion pattern that is very difficult to be emulated by attack presentations. A different database was collected to assess the method including genuine fingerprint and silicon attack presentations. The database was collected under controlled presentation instructions where the fingerprint/PAI is placed on the sensor's surface, then pressure is applied in different directions. Under those conditions, the minutia movement represents the global distortion, and a sequence of paired minutia before and after distortion is used to calculate the parameters