Gait-DenseNet: A Hybrid Convolutional Neural Network for Gait Recognition and model-free approaches. Model-based approach [1, 2, 3, 4, 5] models the human body by using

—Gait is the walking posture of a human, which involves movements of joints at upper limbs and lower limbs of the body. In gait recognition, the human appearance changes are taken into account, which makes it easier to differentiate every individual. However, covariates such as viewing angle, clothing and carrying condition act as the crucial factors that affect the gait recognition process. In this work, a hybrid model that integrates pre-trained DenseNet-201 and multilayer perceptron is presented. The method first extracts the gait energy image by windowing the gait binary images. Sub-sequently, transfer learning of the pre-trained DenseNet-201 model is leveraged to learn the representative features of the gait energy image. A multilayer perceptron is then used to further capture the relationships between these features. Finally, a classification layer assigns the features to the associated class label. The performance of the proposed method is evaluated on CASIA-B dataset, OU-ISIR D dataset and OU-ISIR Large Population dataset. The experimental results show significant improvements on all the datasets compared to the state-of-the- art methods.