Gait recognition plays an important role in the area of biometric recognition. Despite that much progress has been made for gait recognition in recent years, most of them are based on lateral-view gait characteristics. These methods usually require a large data collection area to capture full gait sequences, which are only applicable in wide outdoor spaces. In this paper, we propose a new frontal-view method for human gait recognition based on gait dynamics and deep transfer learning. The frontal human silhouettes are characterized with four kinds of time-varying gait features, namely the lower limb length ratio, lower limb area ratio, swing area of lower limb, and swing angle of lower limb. In the training phase, gait dynamics underlying the aforementioned time-varying gait features is approximated by radial basis function neural networks through deterministic learning theory. This kind of dynamics information reflects the essential dynamical attribute of human walking and is insensitive to the deformation of gait silhouettes caused by frontal-view walking process. To further improve the recognition rates, the extracted gait dynamics and deep transfer learning technique are incorporated, and a deep transfer learning based feature fusion strategy are investigated for human identification task in the recognition phase. To further improve the robustness against different walking conditions, three kinds of gait representations are fused to provide a comprehensive characterization of human walking movement. Finally, comprehensive experiments are carried out on the well-known public gait databases to demonstrate the recognition performance of the proposed algorithm.