Deep Convolutional Feature-based Gait Recognition Using Silhouettes and RGB Images

Today, many different biometrie features are used for human identification. Unlike biometrie features, such as eye, iris, ear, and fingerprint, gait biometrics enables recognition from long distance and low resolution images. In this paper, different design choices for a deep learning-based gait recognition system are investigated in detail. Some preprocessing steps, such as human silhouette extraction and gait cycle calculation are eliminated to make the system suitable for practical applications. To assess different input types' effect on the gait recognition performance, both binary silhouettes and RGB images are given as input to the network. To observe the contribution of transfer learning, we fine-tuned a pre-trained generic object recognition model with the CASIA-B gait dataset and performed experiments on the OU-ISIR Large Population gait dataset. To observe the effect of pose variations, we conducted experiments for both identical-view and cross-view conditions. Successful results are obtained, especially for cross-view gait recognition, compared to different approaches for gait recognition.