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In smart fields, security measures are taken to protect people against threats that may arise by using technology and to provide crisis management, and the functions of measuring area security and ensuring its effectiveness are carried out. As an element of this measurement, it is thought that person recognition may be the most important factor in the future. It is seen that deep learning-based algorithms, which can provide fast and high-accuracy results with many data, will be an integral part of this sector in the future as they are today. However, when the literature is examined, it is understood that the number of research in which Deep learning algorithms are used in order to increase the success of the studies in this direction and the system practicality is insufficient. For this reason, in this study, deep learning was used to recognize people by using the walking data of 15 people obtained thanks to wearable sensors. Since the increase in the diversity of the data will positively affect the learning of the created model, data augmentation has been made and these data have been classified in the MLP-based DNN model. The results were statistically analyzed and showed that this model exhibited excellent performance in person recognition from walking data. In addition, the ACC rate was found to be 100%, and it proved that the method used to increase the data also produced successful results in walking data. It is thought that the success of the study can provide important perspective support to new studies for smart fields in the literature. |
