# The Impact of Pressure on the Fingerprint Impression: Presentation Attack Detection Scheme
## 2 Related Work  
### Fingerprint Presentation Attack Detection  
As mentioned previously, PAD mechanisms are categorized as hardware and software mechanisms. Hardware-based mechanisms are designed to capture signals, such as blood pressure, pulse oximetry, temperature, etc. [12; 13; 14], assuming that these signals represent natural finger characteristics. Although this category imposes additional hardware cost, it can detect presentation attacks with remarkable accuracy. [PERSON] et al. [15] used multispectral imaging within the short-wave infrared (SWIR) spectrum and fine tuned convolutional neural networks (CNNs) to study a small dataset of bona fide and 12 presentation attack instrument (PAI) species. In their experiment, the authors reported 100% classification accuracy. The investigation was extended later using a larger dataset that includes 35 PAI species showing that a combination of two CNNs achieves attack presentation classification error rate (APCER) of 7% at 0.1 bona fide presentation classification error rate (BPCER). [PERSON] et al. (2016) proposed using microscope imaging with special lighting conditions to acquire fingerprint presentations then investigated the scale invariant feature transform (SIFT) through the bag of words approach in order to extract the PAD features; the experiment reported an APCER of 1.78 at 1.33% BPCER. [PERSON] and [PERSON] (2017) used time domain spectroscopy setup in the reflection configuration to study the interaction of terahertz radiation with the friction ridge skin of the finger. A deep learning model was implemented to classify attacks and bona fide presentation achieving 98.8% classification accuracy. On the other hand, software-based methods seek to define discriminative features from static or dynamic fingerprint captures. In 2014, [PERSON] et al. (2018) proposed an image quality-based PAD mechanism for fingerprint, face, and iris modalities with APCER = 13% and BPCER = 14%. [PERSON] et al. (2019) analyzed the SIFT using the bag of words (BoW) approach. The mechanism was evaluated using the LivDet 2011 dataset and reported APCER = 5% at BPCER = 4.3%. In [PERSON] et al. (2019), [PERSON] et al. extended their work by proposing a PAD mechanism based on the combination of local dense-SIFT image descriptors and three different feature encoding approaches: Fisher vector (FV), vector locally aggregated descriptors (VLAD), and BoW, reporting the best results in the LivDet 2019 competition with an overall accuracy of 96.17%. The challenge of detecting PAI species that were not considered in the training phase was discussed by [PERSON] et al. (2011) where the investigation proposed a scheme for the automatic detection and adaptation of a PAD mechanism to PAIs fabricated using novel materials. The latter scheme was reported to be robust to detect novel PAI with a limitation of potentially misclassifying low quality genuine fingerprints. Moreover, in the context of dynamic PAD mechanisms, we analyzed in our previous work ([PERSON] et al., 2019) the variation of first order statistics in fingerprint videos of different sensing technologies where the mechanism achieved 18.1% BPCER for the thermal sensing and 19.5% BPCER for the optical sensing at 5% APCER for both. We extended the experiment by utilizing spatio-temporal feature extractors that consolidate the spatial fingerprint features with the temporal variations, which improved the accuracy to 3.89% BPCER for the thermal sensing and 1.11% BPCER for the optical sensing at 5% APCER for both ([PERSON] et al., 2019). Additionally, deep learning has received much attention over the last decade due to its capability of extracting deep features that achieve significant accuracy improvement over the handcrafted features. In 2016, [PERSON] et al. (2016) proposed one of the first deep learning models in the context of fingerprint PAD using three CNNs. The authors concluded that their mechanism achieved the highest classification accuracy (97.1%) when compared to previous investigations. More recently, [PERSON] et al.