## 6 Discussion  
Prevalent DL and ML algorithms and their usage with various biometric authentication schemes have been reviewed in depth throughout this paper. After close analysis of each biometric dynamic, it is clear that the careful selection of algorithms can result in better performance, and that specific algorithms are more adept to working with different types of biometric data. Considering deep learning authentication schemes, CNN and RNN dominate physiological and behavioral dynamics. Both are proficient with touch, motion, and gait dynamics, as demonstrated in Table 6. RNN is observed to be especially useful with keystroke dynamics, which was anticipated given that RNN is known to perform well with sequential data such as text. CNN was recognized to be proficient with physiological data, performing with higher metrics compared to other algorithms with facial, ocular, and fingerprint-based authentication schemes. This trend is consistent with CNN, given that CNN is majorly used in image recognition, and physiological dynamics commonly utilize image-captured inputs for their authentication schemes. Overall, CNN and RNN are the two most used algorithms within the studies examined within this paper as seen in Table 6 and can produce authentication results with a high accuracy and capability with mobile devices. With the rise of dataset quantity and quality in recent years, DL algorithms have been able to surpass ML in many studies and prompt the creation of more advanced and capable mobile authentication models.  
As previously established in [4], SVM is a strong contestant for mobile behavioral biometric classification. This was once again observed within studies in this paper. SVM was popular in studies working with touch, motion, and keystroke dynamics schemes, outperforming many other ML algorithms. RF was found to be a close second, also providing adequate performance with these schemes with favor to keystroke dynamics. It is notable that SVM was also observed to be capable as a classifier in physiological dynamics such as facial authentication when paired with CNN features. This brings the discussion of a particular phenomenon within mobile biometric authentication to light.  
Hybrid authentication systems are on the rise, as demonstrated by many models within this paper. Many schemes using hybrid architectures involve using one algorithm for feature extraction and another to perform classification with those features. These architectures have presented themselves in pairs of DL-DL as well as DL-ML. CNN was exceedingly popular as a feature extractor for many of these hybrid authentication systems. Within gait dynamics, it was observed that a CNN + LSTM system was the most popular choice out of any architecture, with CNN used for feature extraction and LSTM for classification. CNN + LSTM often outperformed standalone LSTM and CNN models as demonstrated by [69, 70, 71]. Within facial authentication, it was observed that a CNN + SVMarchitecture was prevalent, wherein CNN was used for feature extraction and SVM was used for classification. In studies [79, 80, 81], different combinations of features with different classifiers were analyzed with facial authentication. In these studies, CNN features with SVM classification outperformed other combinations. Motion-sensing dynamics authentication systems in studies [32, 33, 34, 35, 36] also used CNN + SVM hybrid architectures. SVM was found to be the best performing classifier with CNN features in studies [32, 36]. In study [34], using CNN features with SVM performed better than with handcrafted features or LSTM-based features. Comparing their hybrid model with non-hybrid comparison studies, researchers of [35] found their CNN + SVM to have a better accuracy. When analyzing these trends, it is evident that hybrid architectures are promising, with mobile biometric authentication able to enhance the authentication performances of many classifiers with CNN features.