36 papers with code • 2 benchmarks • 7 datasets
( Image credit: GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition )
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Most implemented papers
GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition
In this paper we present a novel perspective, where a gait is regarded as a set consisting of independent frames.
Human Gait Database for Normal Walk Collected by Smartphone Accelerometer
Gait recognition is the characterization of unique biometric patterns associated with each individual which can be utilized to identify a person without direct contact.
GaitPart: Temporal Part-Based Model for Gait Recognition
Gait recognition, applied to identify individual walking patterns in a long-distance, is one of the most promising video-based biometric technologies.
GaitGraph: Graph Convolutional Network for Skeleton-Based Gait Recognition
However, silhouette images can lose fine-grained spatial information, and most papers do not regard how to obtain these silhouettes in complex scenes.
Towards a Deeper Understanding of Skeleton-based Gait Recognition
Gait recognition is a promising biometric with unique properties for identifying individuals from a long distance by their walking patterns.
GaitGL: Learning Discriminative Global-Local Feature Representations for Gait Recognition
GFR extractor aims to extract contextual information, e. g., the relationship among various body parts, and the mask-based LFR extractor is presented to exploit the detailed posture changes of local regions.
Deep Learning-Based Gait Recognition Using Smartphones in the Wild
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.
Robust Cross-View Gait Recognition with Evidence: A Discriminant Gait GAN (DiGGAN) Approach
Gait as a biometric trait has attracted much attention in many security and privacy applications such as identity recognition and authentication, during the last few decades.
Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait
To our knowledge, this is the state-of-the-start performance in Parkinson's gait recognition.
Biometrics Recognition Using Deep Learning: A Survey
Deep learning-based models have been very successful in achieving state-of-the-art results in many of the computer vision, speech recognition, and natural language processing tasks in the last few years.