# CNN-Based Multi-Factor Authentication System for Mobile Devices Using Faces and Passwords
###### Abstract
Applying CNNs in the context of MFA improves its accuracy and security--aspects that have fueled the consistent publication of related research. For instance, [PERSON] and [PERSON] [11] attempted to solve the anti-spoofing problem through simultaneously testing the user's liveness along with a face image and proposed a multi-task CNN. [PERSON] et al. [12] implemented multi-modal biometric authentication using two steps to pass both ECG and fingerprint tests and then fused the results. [PERSON] et al. [13] proposed a secure authentication system which compares face images captured by CCTV with face images using a smartphone. [PERSON] et al. [14] proposed a secure authentication system using a CNN for face and acoustic features, while [PERSON] et al. [15] learned fingerprint and facial features using Google Colaboratory's CNN model. [PERSON] et al. [16] learned fingerprint and finger-vein images using VGG-19 and proposed adding a hashing method for multimodal biometric templates to increase security. [PERSON] et al. [17] used passwords, human hand impedance, and geometry data as factors, and they developed a CNN to enable user recognition. [PERSON] et al. [18] proposed electroencephalogram (EEG) identity authentication through transforming EEG signals into three-dimensional data that were then learned using a multi-scale convolutional structure. There have also been cases where CNNs have been applied in a multi-input system for continuous authentication, with the aim being to monitor login sessions. To identify smartphone users, [PERSON] et al. [19] and [PERSON] et al. [20; 21] applied a CNN through transforming frequency domain data created by an accelerometer and gyroscope using the fast Fourier transform method. [PERSON] et al. [22] used a CNN for ECG classification, while [PERSON] et al. [23], [PERSON] et al. [24], and [PERSON] et al. [25] proposed the use of multi-input CNNs for human activity recognition. CNN-based multi-factor research is also being conducted in the medical field, where precise judgment is required. Some papers have proposed increasing medical precision through using CNNs with a multi-factor, multi-input structure. For example, [PERSON] et al. [26] reported a cardiac image approach that creates a high-resolution image through inputting several low-resolution images, which is helpful in medical treatment. [PERSON] and [PERSON] [27] revealed that CT scans and X-Ray COVID-19 images have been used for multi-input CNN learning to diagnose COVID-19, increasing the accuracy to 98%. [PERSON] et al. [28] found that the accuracy of glaucoma assessment can be improved to 99.1% using fundus images in a multiple input system that employs VGG19, a CNN architecture. Additionally, to ensure safe healthcare services, [PERSON] et al. [29] proposed a telecare medical information system (TMIS) based on machine learning to maintain anonymity in telemedicine. Systems demonstrating that a CNN-based multi-input system can be applied to environmental management have also been proposed. For instance, [PERSON] et al. [30] proposed a 4D fractal CNN model that can predict water quality using lake images. [PERSON] and [PERSON] [31] recommended applying such an approach for short-term photovoltaic power prediction through multi-input CNN learning of temperature, solar radiation, and PV system output data. The objective of this study is to propose an MFA system that satisfies the following:  
1. A mobile-applicable lightweight MFA system;
2. A multi-modal system that can use both image and text simultaneously, in order to use a text password as a factor;
3. A system where passwords can be used safely;
4. A system in which the weights of three or more factors are applied equally. To achieve these objectives, a CNN-based system was developed. A CNN-based design was chosen as people often look at other people's faces and appearances to determine who they are. In addition, there are many studies on CNN-based authentication systems, allowing the results of this paper to be comparatively evaluated.