# CNN-Based Multi-Factor Authentication System for Mobile Devices Using Faces and Passwords
## 2 Related Works
[PERSON] and [PERSON] [40] proposed biometric authentication using a CNN structure called the parallel Sparrow Search Algorithm-Deep Belief Network (SSA-DBN), consisting of seven layers, in which the three factors are ECG signals, fingerprints, and sclera images. Multimodality was achieved through inputting signals and images simultaneously, and SSA-DBN classified data from 80 people with an accuracy of 97.13%. For these factors, score-level fusion was performed after learning was complete. [PERSON] et al. [41] proposed an MFA system using a parallel CNN with three factors: face images, fingerprints, and iris images. Data from 106 people were classified with 99.92% accuracy using four layers in the CNN. As this comprises a lightweight structure with only four layers, it can be applied in mobile devices. However, as only images were learned, it is difficult to assert that a multi-modal approach was implemented. The MMCNN proposed in this paper is a parallel multi-input, multi-factor MFA with three factors: face images, auxiliary images, and password text. It has a multi-modal structure that can learn text and images simultaneously. It has only four layers, and, thus, can be applied in mobile devices. In addition, data security is improved through the use of a secure password image production method for text passwords. The review of existing CNN-based MFA research papers published to date revealed that they do not meet all the functions implemented in the proposed MFA system. These points are summarized in Table 1.  
Researchers have also proposed MFA systems that can increase accuracy and security levels through mixing a CNN with another method. Table 1 also lists these kinds of MFA systems. For example, [PERSON] et al. [42] proposed two-factor authentication using echo signals reflected from facial landmarks and a 3D facial contour, which they named EchoPrint. They explained that this is a secure method, as the echoed acoustic features are not easily spoofed. The two factors were subjected to parallel feature-level concatenation, and a CNN with six layers was used to extract the features of echo signals. The support vector machine (SVM) classifier was then applied to classify the data of 50 people with an accuracy of 93.75%. [PERSON] and [PERSON] [43] implemented facteureID, an MFA system, using face images, OTP text, and handwriting gesture images. Face and gesture images were learned using a CNN containing seven layers, following which the one-time password (OTP) confirmation procedure in text format was used to classify 20 people with an accuracy of 96%. The method for checking the three factors was sequential authentication, where the face image was checked first, followed by the OTP text and, finally, the gesture. When the authentication process of the MFA system is performed sequentially, a separate CNN is needed for each factor. The difference between the sequential authentication process and the parallel structure is depicted in Figure 1.  
[PERSON] and [PERSON] [44] proposed an MFA system using password text and a signature image. Employing the sequential authentication system, they first confirmed the password and then the signature image. The signature image was learned using a CNN with seven layers, and the identity was authenticated using a hashed password to strengthen the security. This two-factor authentication system exhibited an accuracy of 99% when used in a sample of 200 people. [PERSON] et al. [45] implemented a multi-input two-tier structure for anti-spoofing. In the first tier, each image was hashed to increase the security level and enable anti-spoofing, and the hashed images were learned with a CNN. The sequential authentication system was applied in the order of fingerprint, finger-vein, and face images.