# CNN-Based Multi-Factor Authentication System for Mobile Devices Using Faces and Passwords
## 2 Related Works  
For the dual purpose of increasing accuracy and security, researchers have continually proposed novel recognition and authentication systems using multiple inputs and factors based on CNN architectures. [PERSON] and [PERSON] [32] proposed the DeepWriter model, with a CNN structure containing seven layers, in order to identify writers from their handwriting. Through testing, they distinguished 300 writers with an accuracy of 99.01%. A method was then employed to input two handwritten images simultaneously, and the two input images were combined with an element-wise sum before the softmax classifier after passing through the seven layers. Utilizing feature-level concatenation, [PERSON] et al. [33] developed a CNN with a seven-layer structure and simultaneously input three images of the same flower to classify flowers into three classes. The CNN displayed an accuracy of 89.6%. [PERSON] et al. [34] proposed a system that detects pedestrian movement and identifies pedestrians. The researchers reported that gait recognition can be performed using accelerometer signals and signals generated from a gyroscope. Data from 744 people were classified, and an accuracy of 96.2% was achieved using a CNN structure with five layers. Two input factors were learned in parallel and a feature-level fusion method was used. Attempts have also been made to improve the accuracy of medical diagnoses with CNN-based multi-input systems. For instance, [PERSON] et al. [35] proposed a system to detect thyroid cancer using T2-weighted magnetic resonance imaging (MRI) images and apparent diffusion coefficient (ADC) map images. A multi-input CNN called the computer-aided design (CAD) system was utilized, and two factors of the data were learned using a parallel structure, which were then used for feature-level concatenation. In their experiment, 49 cases were distinguished and an accuracy of 87% was recorded when using a CNN structure with four layers. Through experiments, the researchers revealed that their proposed multi-input system was suitable for mobile devices, as the CNN had relatively high accuracy compared with the existing AlexNet and ResNet18 while having a lightweight structure consisting of only four layers. Additionally, [PERSON] et al. [36] reported that CNN learning can be used to diagnose respiratory symptoms through two inputs: respiratory audio sound and demographic vector. These two factors utilized feature-level concatenation through parallel learning and exhibited an accuracy of 87.3% in distinguishing 126 subjects. The CNN used had a deep CNN structure with nine layers. Multi-modality was implemented using sound and vector data. Other researchers have strived to use multiple signal inputs. They found that the signals can be factors in the convolutional layers. For instance, [PERSON] et al. [37] used a CNN to learn three signals--electrocardiography (ECG), electrodermal activity (EDA), and respiratory activity (RSP)--for emotion recognition. These three factors underwent parallel learning to achieve feature-level concatenation. The experiment was conducted with a CNN structure containing eight layers, and five types of emotions were classified with an accuracy of 83.8%. [PERSON] et al. [38] proposed a system using ECG and photoplethysmography (PPG) data in a CNN structure for two-factor authentication. The two signals were fused during the parallel learning process to increase security. The CNN had 17 layers and classified 66 data samples with 99.8% accuracy. [PERSON] et al. [39] proposed a biometric authentication system utilizing Resnet50 for learning using two factors: ECG signals and fingerprint images. A multi-modal system was implemented using signal and image data as factors, and data samples from 70 people were classified with an accuracy of 98.6%. The two factors were concatenated in a feature-level manner.