# Human Skeleton Data Augmentation for Person Identification over Deep Neural Network
###### Abstract  
With the advancement in pose estimation techniques, skeleton-based person identification has recently received considerable attention in many applications. In this study, a skeleton-based person identification method using a deep neural network (DNN) is investigated. In this method, anthropometric features extracted from the human skeleton sequence are used as the input to the DNN. However, training the DNN with insufficient training datasets makes the network unstable and may lead to overfitting during the training phase, causing significant performance degradation in the testing phase. To cope with a shortage in the dataset, we investigate novel data augmentation for skeleton-based person identification by utilizing the bilateral symmetry of the human body. To achieve this, augmented vectors are generated by sharing the anthropometric features extracted from one side of the human body with the other and vice versa. Thereby, the total number of anthropometric feature vectors is increased by 256 times, which enables the DNN to be trained while avoiding overfitting. The simulation results demonstrate that the average accuracy of person identification is remarkably improved up to 100% based on the augmentation on public datasets. Keywords:anthropometric feature; data augmentation; deep learning; deep neural network; human skeleton; person identification +
Footnote †: journal: Medical  
ears and remove the facemask and hat to reveal their face. For voice recognition, the user needs to say a few sentences into a sensor even if they are reluctant to say anything. By contrast, gait recognition has the advantage of being performed without the user's cooperation, wherein the user only has to walk in the usual way. Because the user's gait data can be obtained from a video filmed from a far or close distance, the person identification process is performed in such a way that the user is unaware and does not feel uncomfortable. Owing to such an advantage, gait recognition has gained significant interest of researchers in the computer-vision community. Early studies, including [11; 12; 13; 14; 15], adopted the approach of extracting the user's gait information from the silhouette sequence. In addition, in most of these studies, only the silhouette sequence extracted from a video in which a walking person was photographed from a side view was used. It may be relatively easy to identify a person using the gait information extracted from the side view of a walking person. However, from the frontal viewpoint, the ability to identify the person accurately is limited. The software development kit (SDK) of the Microsoft Kinect sensor was released in May 2011 [16]. Using the SDK, the user can acquire three-dimensional (3D) position data for 20 joints of the human body from the Kinect sensor [17; 18; 19]. In comparison with the human silhouette, the use of joint position enables view-invariant person identification. Using this advantage, several studies on person identification methods have been conducted using the Kinect sensor. [PERSON] et al. [20] acquired human skeleton data from walking and running people to achieve person identification. The authors calculated the difference in motion between unknown and authenticated people using their skeleton data. [PERSON] et al. [21] compared the performance of silhouette- and Kinect skeleton-based person identification methods, which revealed that using the skeleton data was a more reliable method for person identification. The use of skeleton data created a new modality for biometric features, called the anthropometric feature, for person identification. In [22; 23; 24; 25; 26; 27; 28], the authors proposed the use of anthropometric features for the person identification task. In most of these studies, several machine-learning algorithms, such as \(k\)-nearest neighbor (\(k\)-NN), support vector machine (SVM), and multi-layer perceptron (MLP), were used to identify individuals using the anthropometric features.