View-Invariant Gait Recognition Through Genetic Template Segmentation
TLDR
Experimental results depict that this approach significantly outperforms the existing implementations of view-invariant gait recognition and GEI seems to exhibit the best result when segmented with this approach.Abstract:
Template-based model-free approach provides by far the most successful solution to the gait recognition problem in literature. Recent work discusses how isolating the head and leg portion of the template increase the performance of a gait recognition system making it robust against covariates like clothing and carrying conditions. However, most involve a manual definition of the boundaries. The method we propose, the genetic template segmentation, employs the genetic algorithm to automate the boundary selection process. This method was tested on the gait energy image (GEI), gait entropy image, and active energy image templates. GEI seems to exhibit the best result when segmented with our approach. Experimental results depict that our approach significantly outperforms the existing implementations of view-invariant gait recognition.read more
Citations
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Journal ArticleDOI
Vision-Based Gait Recognition: A Survey
TL;DR: This paper describes measuring metrics that can be used to measure the performance of gait recognition model under verification and identification mode and identifies the future perspectives in gait recognized and also outlines the proposed work.
Journal ArticleDOI
Robust gait recognition: a comprehensive survey
TL;DR: A comprehensive overview of existing robust gait recognition methods is provided to provide researchers with state of the art approaches in order to help advance the research topic through an understanding of basic taxonomies, comparisons, and summaries of the state-of-the-art performances on several widely used gait Recognition datasets.
Journal ArticleDOI
Multi-View Gait Image Generation for Cross-View Gait Recognition
TL;DR: Wang et al. as mentioned in this paper used a Multi-view Gait Generative Adversarial Network (MvGGAN) to generate fake gait samples for cross-view gait recognition.
Journal ArticleDOI
View-Invariant Gait Recognition With Attentive Recurrent Learning of Partial Representations
TL;DR: A network that first learns to extract gait convolutional energy maps (GCEM) from frame-level convolutionAL features then adopts a bidirectional recurrent neural network to learn from split bins of the GCEM, thus exploiting the relations between learned partial spatiotemporal representations.
Journal ArticleDOI
Human gait recognition using GEI-based local multi-scale feature descriptors
TL;DR: A novel gait recognition approach capable of selecting information characteristics for human identification under different conditions including normal walking, carrying a bag and wearing a clothing for different angles of view; thereby enhancing the recognition accomplishment.
References
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Journal ArticleDOI
Individual recognition using gait energy image
TL;DR: Experimental results show that the proposed GEI is an effective and efficient gait representation for individual recognition, and the proposed approach achieves highly competitive performance with respect to the published gait recognition approaches.
Proceedings ArticleDOI
A Framework for Evaluating the Effect of View Angle, Clothing and Carrying Condition on Gait Recognition
Shiqi Yu,Daoliang Tan,Tieniu Tan +2 more
TL;DR: A framework consisting of a large gait database, a large set of well designed experiments and some evaluation metrics to evaluate gait recognition algorithms is proposed.
Book ChapterDOI
Removing the Genetics from the Standard Genetic Algorithm
Shumeet Baluja,Rich Caruana +1 more
TL;DR: An abstraction of the genetic algorithm, termed population-based incremental learning (PBIL), that explicitly maintains the statistics contained in a GA''s population, but which abstracts away the crossover operator and redefines the role of the population results in PBIL being simpler, both computationally and theoretically, than the GA.
Proceedings ArticleDOI
Silhouette-based human identification from body shape and gait
TL;DR: This baseline recognition method provides a lower bound against which to evaluate more complicated procedures and is evaluated on four databases with varying viewing angles, background conditions, walking styles and pixels on target.
Journal ArticleDOI
Automated person recognition by walking and running via model-based approaches
TL;DR: Results show that both gaits are potential biometrics, with running being more potent than walking, and a phase-weighted Fourier description gait signature by automated non-invasive means is derived.
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