## 3 Literature Review
### Physiological Biometrics
#### 3.2.3 Other Physiological Dynamics  
As previously mentioned, biometrics are currently the most prevalent in mobile biometric research, but they are not the only dynamics that have produced viable results for authentication schemes. Many studies such as [92, 93, 94, 95] have utilized fingerprint characteristics for mobile authentication models. In [92], researchers proposed a partial fingerprint recognition scheme with DL. [PERSON] et al. recognized that modern mobile fingerprint scanners were not able to capture the entire fingerprint; thus, they created an algorithmic model built specifically for the recognition of partial fingerprint images. Their model used CNN-ResNet architecture with cross-entropy and contrastive loss functions. This model was trained using partial fingerprint images from the NCMU-FR and the NIST-DB4 databases, consisting of over 9 k fingerprint images in total, with 5 k collected from a touch fingerprint scanner. For evaluation, their model performance was compared to a reference study that also used CNN architecture for partial fingerprint recognition. Results found that the proposed model was able to outperform the reference model with 91.8-93% accuracy on the NIST-DB4 database. Study [93] proposed FINAUTH, a model aimed to complement fingerprint authentication models and resist puppet attacks with fingertip-touch characteristics. This model utilized sensors common in mobile devices such as accelerometer, gyroscope, and magnetometer to capture the fingertip-touch characteristics. For feature extraction, FINAUTH was tested with time-and-frequency domain features and CNN-learned features with Leaky-ReLu activation functions. For classification, the study compared the performance of four methods: Pearson's correlation coefficient (PCC), OC-SVM, LOF, and IF. For the collected dataset, 90 subjects and 15 attackers were recruited separately and tasked with performing fingerprint smartphone unlock in different holding positions. Results of their study found that CNN + LOF achieved the most reliable authentication performance and that CNN features were more discriminative than time-frequency domain features. In the sitting scenario, CNN + LOF achieved 97.99% BAC and 0.86% FAR. With non-overlapping subjects in training and testing, CNN-LOF achieved a 95.34% balanced accuracy (BAC) and 0.9805 AUC. Further testing with CNN-LOF found that FINAUTH was able to resist replica, puppet, and mimicry attacks. For study [94], researchers proposed a framework to detect spoofing in fingerprint recognition for mobile banking applications. Their model architecture first used histogram equalization on fingerprint images to increase contrast and uniformity before applying a CNN for feature extraction and classification. The CNN architecture included multiple convolutional layers and a two-unit SoftMax layer for the two-class problem (live vs. spoofed fingerprint). A total of five different databases consisting of real and spoofed fingerprint images was used to train their model. Results found that their CNN architecture was able to achieve >99.00% recognition accuracy on all databases, outperforming comparison SOTA CNN architectures. Spoof-detection rates were overall improved. C2 CL, a contactless fingerprint matching mobile authentication system, was proposed in study [95]. For preprocessing, their method involved a U-net segmentation network, multiple image enhancements, and distortion correction and scaling performed by a spatial transformer network (STN). After preprocessing, a CNN architecture based on deep print was fine-tuned for the contact-contactless scenario and used to perform representation extraction. Finally, Verifinger 12.0 Software Development Kit (SDK) was used to extract minutiae representations from the contactless fingerprint images. A match score was computed with weighted fusion for classification/matching. Multiple databases consisting of over 50k contact/contactless fingerprint images were used for the training and testing of their model.