# A Deep-Learning Approach for Identifying a Drunk Person Using Gait Recognition
## 1 Introduction
However, since image data responds sensitively to the recording environment, it is necessary to select an appropriate data processing method to obtain accurate data in various situations. In this study, we aimed to identify drunk individuals in places of work in order to decrease workplace accidents. We employed the vision-based method because it can identify a drunk person simply passing in front of a camera, without wearing any sensors. To improve accuracy, we used the GEI method, which is simple, able to perform quick calculations, and is not sensitive to recording conditions. We analyzed GEI using deep learning to identify drunk individuals. The data processing method can be divided into a model-based method that analyzes joint movement and an appearance-based method that utilizes silhouette contours obtained through a background subtraction method. The model-based method has the advantage of low sensitivity to camera direction and illuminance, as well as changes in the user's clothes, but has the disadvantage that the calculation is complicated and takes a significant amount of time. Since rapid data processing is important for applying the present technology to a large number of subjects, a simple and fast appearance-based method of calculation is used. The general appearance-based method has a disadvantage in that it is sensitive to recording conditions such as illuminance and the user's attire, but it can be solved by using the gait energy image (GEI) method, which removes the background and averages each pixel of the binarized images [23; 24]. As methods for GEI-processed gait data identification, deep learning models that can automatically identify gait by an algorithm and can learn patterns in data and make decisions without the need for manual feature engineering will be used [26]. Amongmany deep learning models, a convolutional neural network (CNN), one type of neural network, is commonly used in the image identification field [27; 28]. One of the reasons why CNNs are widely used is that they do not require manual tuning (manual-tuning-free), while legacy object detection algorithms require a series of image processing steps, such as contrast optimization or contouring detection. Thus, CNN is a deep learning model that exhibits the characteristics of easy model training because it has fewer connections and parameters than other traditional feedforward neural networks of a similar network size. In addition, since CNN has been used for gait analysis using non-model-based data and its possibility has been verified, this study intends to analyze gait by using it in this study [23]. However, CNN requires a significant amount of data, because it is difficult for it to run normally when the training dataset is not large. To solve this problem, IA (image augmentation) will be used [29]. In [PERSON]'s study attempting to implement an algorithm to identify people in images, an experiment was conducted by increasing the quantity of data using the IA technique make utilization of the CNN model feasible [30]. The IA method used in this study included rotation (0-180\({}^{\circ}\)), tilting, magnification, brightness, and left/right inversion, and through this, the dataset of 200 people was increased by about 100 times. Afterward, as a result of CNN's deep learning analysis, the accuracy improved to about 96.23% after using IA compared to 82% when IA was not used, which was similar to the accuracy when using a dataset of 800 without using IA. Therefore, it can be confirmed that a performance similar to that of the analysis when using a large dataset is possible using IA, even if the quantity of data is relatively large. Thus, this study also intends to determine the effect of improving accuracy by using IA. In order to analyze not only the identification using deep learning, but also the unique characteristics of a drunk gait, we will extract parameters and compare the differences in gait characteristics according to whether or not a person has consumed alcohol.