20 papers with code • 1 benchmarks • 6 datasets
( Image credit: GaitSet: Regarding Gait as a Set for Cross-View 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.
Gait recognition is a promising biometric with unique properties for identifying individuals from a long distance by their walking patterns.
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.
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 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.
Gait recognition, applied to identify individual walking patterns in a long-distance, is one of the most promising video-based biometric technologies.
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 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.