Human traits such as the face, voice, and gait are commonly used for biometric-based person recognition. Although gait is one of the most practical traits for video-based surveillance and forensics, this approach is susceptible to intra-subject variations. Researchers have proposed a new convolutional neural network-based approach to gait recognition, which exhibits robustness (insensitiveness) to spatial displacement. Their approach outperformed current benchmarks in terms of verification/identification tasks and view differences.