( Image credit: GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition )
In this paper, we present a novel perspective that utilizes gait as a deep set, which means that a set of gait frames are integrated by a global-local fused deep network inspired by the way our left- and right-hemisphere processes information to learn information that can be used in identification.
In this paper we present a novel perspective, where a gait is regarded as a set consisting of independent frames.
Ranked #2 on Multiview Gait Recognition on CASIA-B
Gait recognition, applied to identify individual walking patterns in a long-distance, is one of the most promising video-based biometric technologies.
Ranked #1 on Multiview Gait Recognition on CASIA-B
Specifically, a hybrid deep neural network is proposed for robust gait feature representation, where features in the space and time domains are successively abstracted by a convolutional neural network and a recurrent neural network.
However, silhouette images can lose fine-grained spatial information, and most papers do not regard how to obtain these silhouettes in complex scenes.
Ranked #3 on Multiview Gait Recognition on CASIA-B
To our knowledge, this is the state-of-the-start performance in Parkinson's gait recognition.
In this paper, we propose iLGaCo, the first incremental learning approach of covariate factors for gait recognition, where the deep model can be updated with new information without re-training it from scratch by using the whole dataset.
Feature extractors using similar architectures incorporated into end-to-end models and autoencoders were compared based on their ability of learning good representations for a gait verification system.
In recent years, single modality based gait recognition has been extensively explored in the analysis of medical images or other sensory data, and it is recognised that each of the established approaches has different strengths and weaknesses.