Gait speed based individual recognition model using deep 2-D convolutional neural network

GAIT represents the pattern in which a person walks. We have four types of walking conditions: normal walking, slow walking, fast walking, and normal walking with a bag. GAIT analysis has been beneficial to doctors to analyse the patterns of walking to detect pains and diseases. In this work, we will compare the classification of the four types of conditions of GAIT using 2-D Convolutional neural networks. The CASIA C dataset has been used to train the 2-D Convolutional neural networks that have images captured using thermal infrared night imaging. Using transfer learning, we tested the performance of various CNN algorithms like DenseNet201 and MobileNetV2. The various algorithms provide us with information that the GAIT C dataset is best for the custom CNN model created and we have compared all the results in this paper.