Gait recognition using Deep Learning

Gait recognition is important for identifying suspects in criminal investigations. This study will study the potential of using models based on transfer learning for this purpose. Both supervised and unsupervised learning will be examined. For the supervised learning part, the data is labeled and we investigate how accurate the models can be, and the impact of different walking conditions. Unsupervised learning is when the data is unlabeled and this part will determine if clustering can be used to identify groups of individuals without knowing who it is. Two deep learning models, the InceptionV3 model and the ResNet50V2, model are utilized, and the Gait Energy image method is used as gait representation. After optimization analysis, the models achieved the highest prediction accuracy of 100 percent when only including normal walking conditions and 99.25 percent when including different walking conditions such as carrying a backpack and wearing a coat, making them applicable for use in real-world investigations, provided that the data is labeled. Due to the apparent sensitivity of the models to varying camera angles, the clustering part resulted in an accuracy of approximately 30 percent. For unsupervised learning on gait recognition to be applicable in the real world, additional enhancements are required