The OU-ISIR Gait Database Comprising the Treadmill Dataset

Li et al. proposed DeepGait, which combines deep convolutional features and Joint Bayesian for video sensor-based gait representation, outperforming hand-crafted features and obtaining state-of-the-art results on OU-ISIR large population dataset.

“Further, Li et al. [52] proposed the DeepGait, a model that combines deep convolutional features and Joint Bayesian for video sensor-based gait representation. The model outperformed hand-crafted features, such as GEI, Frequency-Domain Feature, and Gait Flow Image, obtaining state-of-theart results over OU-ISR large population dataset [59].”

Tran et al. proposed an IMU-based gait recognition approach using LSTMs, achieving state-of-the-art performance for verification and identification on whuGAIT and OU-ISIR.

“Recently, Tran et al. [103] proposed an Inertial Measurement Units (IMUs)-based gait recognition approach. The authors employed LSTMs to exploit the temporal information on video sequences, thus extracting hidden patterns inside such sequences. Experiments conducted over whuGAIT [141] and OU-ISIR [59] provided state-of-the-art performance for both verification and identification tasks.”

Shiraga et al. used an architecture similar to LeNet to create a GEI-based recognizer, achieving 91.5% accuracy on the OU-ISIR Large Population dataset.

“In the context of gait recognition, Shiraga et al. [86] used an architecture similar to LENet [49] to create a gleaming-based recognizer through Gait Energy Image (GEI). The model achieved an accuracy of 91 . 5% in the OU-ISIR [59] Large Population dataset.”