# CNN-Based Multi-Factor Authentication System for Mobile Devices Using Faces and Passwords
## 2 Related Works
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**References** & **Method** & **Layers** & **Accuracy (\%)** & **Mobile Devices** & **Modality** & **Secure Data** \\ \hline Proposed MFA System with MMCNN & CNN & 4 & 99.6 & Applicable & Image, Text & Password \\ \hline (2016) Xing and Qiao [32] & CNN & 7 & 99.01 & N/A & Image & N/A \\ \hline (2017) [PERSON] et al. [33] & CNN & 7 & 89.6 & N/A & Image & N/A \\ \hline (2018) [PERSON] et al. [34] & CNN & 5 & 96.2 & N/A & Signal & N/A \\ \hline (2021) [PERSON] et al. [35] & CNN & 4 & 87 & Applicable & Image & N/A \\ \hline (2021) [PERSON] et al. [36] & CNN & 9 & 87.3 & Applicable & Sound, vector & N/A \\ \hline (2022) [PERSON] et al. [37] & CNN & 8 & 83.8 & N/A & Signal & N/A \\ \hline (2022) [PERSON] et al. [38] & CNN & 17 & 99.8 & N/A & Signal & Signal \\ \hline (2023) [PERSON] et al. [39] & CNN & 50 & 98.6 & N/A & Signal, Image & N/A \\ \hline (2023) [PERSON] and [PERSON] [40] & CNN & 7 & 97.13 & N/A & Signal, Image & N/A \\ \hline (2023) [PERSON] et al. [41] & CNN & 4 & 99.92 & Applicable & Image & N/A \\ \hline (2018) [PERSON] et al. [42] & CNN, SVM & 6 & 93.75 & Applicable & Sound, Image & Sound \\ \hline (2018) [PERSON] and [PERSON] [43] & CNN, OTP & 7 & 96 & N/A & Image & N/A \\ \hline (2019) [PERSON] and [PERSON] [44] & CNN, HASH & 7 & 99 & N/A & Image & Password \\ \hline (2019) [PERSON] et al. [45] & CNN, HASH & 22 & 99.75 & N/A & Image & Image \\ \hline (2020) [PERSON] et al. [46] & CNN, RF & 5 & 99.49 & N/A & Image & N/A \\ \hline (2021) [PERSON] et al. [47] & CNN, OTP & 5 & 99.33 & Applicable & Image & Iris image \\ \hline (2023) [PERSON] and [PERSON] [48] & CNN, LSTM & 11 & 98.79 & N/A & Signal & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the proposed MFA system with other MFA systems. Using GoogLeNet with 22 layers as a CNN, data from 50 people were classified with an accuracy of 99.75%. [PERSON] et al. [46] employed a five-layer CNN in parallel to learn fingerprint, finger-vein, and face images. For these three factors, score level fusion was performed after the CNN evaluation results, following which the RF classifier was applied to classify the data of 106 people with an accuracy of 99.49%. [PERSON] et al. [47] proposed the use of OTP and iris images to strengthen security when using bank automated teller machines (ATMs), and devised a system using a CNN with a five-layer structure to learn iris images. This is a sequential authentication system, which performs final authentication using OTP after checking the iris images learned with the CNN. The image itself was encrypted and transmitted to the server to increase the security level, and the data of 60 people were classified with 99.33% accuracy. Utilizing a CNN-LSTM (long short-term memory) structure for fear level classification, [PERSON] and [PERSON] [48] learned electroencephalogram (EEG) and peripheral physiological signal (PPS) data. Feature-level concatenation with a parallel structure was employed, and four fear levels were distinguished with an accuracy of 98.79% using CNN-LSTM with 11 layers. Table 2 presents a list of recent CNN-based MFA systems which have obtained more than 97% accuracy. [PERSON] et al. [38] and [PERSON] et al. [39] suggested systems with 17 layers and 50 layers, respectively, which are not suitable for use in mobile devices. [PERSON] and [PERSON] [40] proposed a relatively lightweight system with seven layers, but did not consider the safety of the data. The system of [PERSON] et al. [41] recognizes only the image as a factor and, so, it cannot implement multi-modality. The system of [PERSON] and [PERSON] [48] has 11 layers, making it unsuitable for mobile devices, and does not consider data safety. [PERSON] [49] proposed a relatively lightweight system with six layers, but it is a complex system with a convolutional layer, gated recurrent unit (GRU) layer, and attention layer; furthermore, it implements only image data, so it cannot implement multi-modality.