## 3 Literature Review
### Physiological Biometrics
#### 3.2.3 Other Physiological Dynamics
Results found that C2 CL was able to outperform previous SOTA verification results with significant improvement in EER values. On the PolyU database, C2 CL achieved a 0.03% EER and 97.74 TAR @ FAR = 0.01%. While few, other studies [96, 97, 98] have found voice and vein dynamics to be viable for mobile authentication. Following the COVID-19 epidemic, study [96] aimed to create a hygiene-aware contactless biometric recognition system utilizing wrist-vein biometrics. Their model was tested as a recognition system with a mobile application, utilizing wrist data captured via near-infrared camera with near-infrared lighting. The model architecture consisted of a CNN algorithm for feature extraction and logistic regression (LR) for classification. CNN architectures such as VGG16, VGG19, ResNet50, and ResNet52 were compared in the evaluation stage. To train and test their models, the UC3M-SV2, UC3M-CV1, PUT, and ImageNet datasets were utilized. Results found that the models were able to produce viable EER rates in verification evaluation. The ResNet152 architecture achieved the lowest EER of 0.33% on the UC3M databases and the VGG16 architecture achieved a 0.58% EER on the raw image UC3M-CV2 database. Researchers of [97] presented a text-independent speaker authentication scheme for mobile devices. Their method involves recording voice audio and extracting features to create a voice sample dataset. Audio data are trimmed for voice segments and normalized, and a linear prediction cepstral coefficient (LPCC) is used for feature extraction. Voice signals before and after trimming are shown in Figure 9. Naive Bayes (NB) was chosen as a classifier due to its low computational load. Three datasets were utilized for model training and testing, with a collected dataset of 11 student volunteers and two datasets from Ted talks and speakers (Ted-LIUM, TIMIT). With the proposed dataset, their scheme achieved 94% accuracy in a quiet environment and 83% accuracy in a noisy environment. A total of 83% accuracy was achieved with the Ted-LIVM dataset and 87% mean accuracy with TIMIT dataset. Results were found to be consistent with comparison schemes. ChestLive, an authentication system utilizing voice dynamics and chest movements, was proposed in [98]. While the user speaks, inaudible acoustic sensing is performed to capture reflected acoustic signals from the chest via the mobile speaker and microphone. These signals are then used to derive channel energy (CE) signals, from which interference is removed to produce a clear chest motion signal suitable for authentication. Mel-frequency cepstrum coefficient (MFCC) is used to extract features that suitably characterize both voice and motion signals. To address the problem of a small training sample set, meta-learning reptile algorithm is used for classification. In reptile, a general neutral network is first trained with a good generalization property and then adapted with new training samples. To construct their dataset, 61 volunteers were instructed to collect voice recordings in different usage scenarios with the mobile device pointed toward their chest. Results found that the model produced an average authentication accuracy of 98.31% and an EER of 2.92%. ChestLive was resilient in different usage scenarios and resistant to replay and impersonation attacks. We have observed from Table 6 that most published studies within behavioral biometrics are focused on the use of touch and motion sensor-based dynamics. Within physiological studies, a large majority invests research into facial and ocular dynamics. Across all studies, DL algorithms are shown as a popular choice for authentication models. Some recent studies have paired DL algorithms into hybrid architectures, performing as a feature extractor for an ML classifier or another DL classifier. It has been observed that many studies are taking advantage of an influx of public datasets containing carefully collected  
Figure 9: Voice signals before and after removal of silence segments [97].