# The Impact of Pressure on the Fingerprint Impression: Presentation Attack Detection Scheme
## 1 Introduction  
Fingerprint recognition provides sustained and secured recognition since the fingerprint is assumed to be unique, collectible, convenient, long term, universal, and acceptable [1]. Despite all these attractive features, it has been shown that latent impressions can be captured by an attacker and used to create a duplicate with the objective of claiming someone's identity [2; 3; 4]. This type of attack is standardized as presentation attack (PA) [5]. In order to eliminate or mitigate the risk of PAs, researchers have been investigating automatic presentation attack detection (PAD) mechanisms that determine whether the presented biometric trait is a bona fide (i.e., genuine) or attack presentation [6]. [PERSON] and [PERSON] [7] classified fingerprint PAD mechanisms into software and hardware mechanisms. The main difference between the two classes is that hardware-based mechanisms require additional hardware conjunction with the sensor to capture additional distinguishing signals. Moreover, we classified PAD mechanisms in our previous work [8] based on the investigated PAD features as these PAD features might be caused by natural phenomena or collateral means. Based on the design of the mechanism, the PAD subsystem may investigate the static or dynamic capture of the fingerprint impression. Although recent PAD mechanisms, based on the static fingerprint image, provide promising performance results, it is still questionable whether the static differences occur due to distinctions between genuine and fake fingerprints or due to the attack potential, which is characterized by the attacker's expertise, motivation, and resources. Toward this end, [PERSON] et al. [3] performed a comparison between the most efficient static PAD mechanisms using two datasets: (1) LiveDet 2019, and (2) a special dataset called \"ScreenSpoof\"that was collected assuming expert attacks. Interestingly, the evaluation results showed that expert attackers reduce the full system accuracy. The impostor attack presentation match rate (IAPMR) increased by 14.1% for the ZJUT mechanism and by 5.5% for the JLW mechanism. Another study was carried out by [PERSON] [9] to evaluating the resistance of fingerprint systems in mobile devices to attacks performed by low and high levels of expertise. Interestingly, the study proved that expert attackers are more likely to be authenticated to the mobile device with a significant difference in IAPMR compared to novice attackers. The design of PAD solutions should rely on genuine distinctions which segregate bona fide from attack presentations. Ideally, genuine fingerprints are supposed to produce good ridge quality, which is characterized by the following attributes: (1) sufficiently wide dynamic range, (2) even density distribution, (3) linearity, (4) no black or white saturation, (5) no significant blur or smudge, and (6) sufficient separation between ridges and valleys [10]. However, multiple behavioral and physical factors, such as inadequate fingerprint presentation and skin conditions, may impact the quality of the acquired image. This effect could be interpreted as a result of the fingerprint's inherent phenomenon. For instance, considering the internal bone position and skin elasticity, applying additional pressure during the fingerprint placement often cause linear and non-linear distortions in the resulting ridge and valley pattern [11]. That can be perceived in the acquired image by thicker ridges, ridge flow distortion, black saturation, and less clarity. In this paper, we investigate the difference between genuine and fake fingerprints through analyzing videos of fingerprint presentation while the capture subject applies additional pressure during the biometric presentation. The dynamic variations in the finger impression reflect the reaction of the natural fingerprint attributes (e.g., elasticity, 3-D shape, perspiration) and the physical characteristics of the different PAI species on the acquired fingerprint video.