# The Impact of Pressure on the Fingerprint Impression: Presentation Attack Detection Scheme
## 7 Conclusions  
In this paper, we examined the influence of pressure in fingerprint presentations and utilized this influence for the purpose of PAD. We studied the dynamic characteristics of genuine and attack presentations through a subjective and objective analysis. Based on those analyses on genuine fingerprints and seven PAI species, we concluded that the reaction to pressure in fingerprint patterns depends on the characteristics of the presented trait, genuine or attack. Bona fide presentations had shown a consistent variation in the pattern, which is highlighted by a homogeneous degradation in the structural similarity in the consequent frames. On the other hand, elastic species (e.g., Play-Doh and gelatin) had shown an extreme variation in the pattern, sometimes the pattern vanishes and a dark region is obtained after pressure. Differently, rigid species (e.g., polish nail and white glue) had shown that pressure might improve the fingerprint pattern and result in high contrast between the ridges and valleys.  
Fingerprint dynamic features were investigated using different dynamic texture descriptors. The utilized feature extractors had demonstrated a significant capability to detect presentation attacks and low false reject rates at the same time. Statistically speaking, when the PAD subsystem is evaluated at 2.5% APCER, i.e., 16 successful attacks out of 630 attacks, BPCER values are 1.11% for the optical sensor, i.e., 2 rejected bona fide presentations out of 180 total, and 2.22% for the thermal sensor, i.e., 4 rejected bona fide presentations out of 180 total.  
In this investigation, we compared the dynamic features of fingerprint presentations under pressure with our previous study on dynamic ordinary presentations [23]. The comparison was carried out by studying the impact of pressure on the classification accuracy of five spatio-temporal features. The initial phase of our experiment confirmed that applying additional pressure triggers the presented fingerprint/PAI manifests distinguishing attributes which significantly improve the PAD subsystem accuracy. Moreover, the comparison analyzed the generalization of each presentation scenario by applying hold-one-out cross validation in order to validate the obtained results on unseen capture subjects. The results confirmed that applying pressure improves the generalizability of the model by reducing the error rates for the unseen subjects. Additionally, the proposed method has illustrated high accuracy for the different sensing technologies where the pros and cons of each sensor were discussed in the previous section.  
This study contributes to the SoA investigation with a deeper understanding of the fingerprint dynamic features and the dynamic characteristics of different attack species.  
Future work should concentrate on studying larger datasets that include a larger number of independent capture subjects. Moreover, the selection of participant/PAI species should include a wide spectrum of skin/materials categories, that is to include very dry skin to very sweaty skin for bona fide and different features for PAIs. From another perspective, the attacker's expertise should be taken into account for conducting a larger-scale evaluation. Meaning that multiple attackers with different capabilities should participate to produce PAs. Finally, we suggest studying fingerprint-specific dynamic features, such as contours development and region of interest variations.  
[PERSON] set the experimental protocol, performed the experiment, and wrote the paper. The experimental results and the paper were analyzed and reviewed by [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the European Union's Horizon 2020 for Research and Innovation Program under Grant 675087 (AMBER).  
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Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
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The authors declare no conflicts of interest.