# A Systematic Survey of Multi-Factor Authentication for Cloud Infrastructure
## 4 Analysis of Authentication Factors
### Face Recognition towards Potential MFA  
The detailed comparative analysis mentioned above concludes that factors of authentication other than facial recognition entail the requirement of specialized and additional \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Ref. (Year)** & **Advantages** & **Limitations** & **Inherent Vulnerability** \\ \hline \multirow{4}{*}{[88] (2016)} & (i) Dynamic and environment & \multirow{4}{*}{(i) Quite complex.} \\  & dependent to choose most & & (ii) Requires large storage. \\  & suited modalities. & (ii) Requires large storage. \\  & (ii) Reduces predictability for & (iii) Registration is lengthy as multiple & Depends on a particular set of \\  & the attacker. & input of biometrics of the user is & approaches selected at a time \\  & (iii) Positive experience regarding & required for password creation. & \\  & usability. & & \\ \hline \multirow{2}{*}{[89] (2017)} & (ii) The data is stored in the smartcard in an encrypted way. & (i) Card may be lost or stolen. & \multirow{2}{*}{Spoofing attack} \\  & (ii) Spoofed fingerprints from other & & \\  & sources may be used. & & \\ \hline \multirow{2}{*}{[90] (2018)} & (i) Secured approach. & (i) Requires downloading of & \multirow{2}{*}{Phishing attack} \\  & (ii) Transmissions are encoded & & different app. & \\ \cline{1-1}  & cryptographically. & (ii) Unavailability of smartphone. & \\ \hline \multirow{2}{*}{[91] (2019)} & (ii) The combination of the fingerprint with & \multirow{2}{*}{Slow and requires high power} & \multirow{2}{*}{NA} \\  & ECG is more secure as it provides the & & consumption; costly. & \\ \hline \multirow{2}{*}{[92] (2020)} & (i) Use of textual and & (i) More cognitive on the human brain. & \multirow{2}{*}{Guessing attack} \\  & figurative credentials. & (ii) Special hardware for & \\  & (ii) Use of human factors. & human-specific biometric verification & \\ \hline \multirow{4}{*}{[23] (2021)} & (i) Cheap and secure in comparison to & (i) Increases cognitive load of the user. & \multirow{4}{*}{Phishing attack} \\  & textual passwords. Anti-key-logger and & (ii) A small approach, not suitable on a & \\ \cline{1-1}  & anti-screen recorder & (iii) More time-consuming. & \\ \hline \multirow{4}{*}{[93] (2021)} & Templates are such that attackers & \multirow{4}{*}{Fingerprints may be stolen from other} \\  & cannot access the original fingerprint & sources and spoofed & Spoofing attack \\ \cline{1-1}  & details from them & & \\ \hline \multirow{4}{*}{[94] (2022)} & (i) Uses Autonomous Inquiry-based & (i) Chatbot needs sufficient training on & \multirow{4}{*}{(i) Impersonation attack.} \\ \cline{1-1}  & Authentication Chatbot (AIAC). & user credential data. & (i) Impersonation attack. \\ \cline{1-1}  & (ii) Human Dynamics Insight And & (ii) It incorporates a huge credential & (ii) Central point of failure of \\ \cline{1-1}  & Metrics segment of the authentication & dataset depending on the number of & the authentication process. \\ \cline{1-1}  & framework is used. & registered users of the system. & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of various MFA Approaches and Inherent Vulnerability. hardware as well as software. They may even require specialized user training for flawless utilization of the authentication factors. At the same time, using such specialized means as an authentication factor is liable to affecting the uninterrupted user experience in a logged-in user session. Considering the user's face to be utilized as a biometric authentication factor, it essentially requires three mutually related processes for facial recognition. The processes are:  
1. Capturing of the image. 2. Detection of the face in the captured image. 3. Comparison of detected facial features with the registered user's stored credentials. For capturing a simple image, a system-connected webcam is recommended to be used without asking for additional hardware components.