Abstract
Gait has unique advantage at a distance when other biometrics cannot be used since they are at too low resolution or obscured, as commonly observed in visual surveillance systems. This paper provides a survey of the technical advancements in vision-based gait recognition. A wide range of publications are discussed in this survey embracing different perspectives of the research in this area, including gait feature extraction, classification schemes and standard gait databases. There are two major groups of the state-of-the-art techniques in characterizing gait: Model-based and motion-free. The model-based approach obtains a set of body or motion parameters via human body or motion modeling. The model-free approach, on the other hand, derives a description of the motion without assuming any model. Each major category is further organized into several subcategories based on the nature of gait representation. In addition, some widely used classification schemes and benchmark databases for evaluating performance are also discussed.
Highlights
In recent decades, much research effort has been devoted to the study of vision-based gait recognition
A gait representation is devised by convolving the modified GEI with Gabor wavelets. Another variant was proposed in Zhang et al (2009), where the dynamic variance parts of the GEI were captured in a Dynamic Gait Energy Image (DGEI)
This paper serves as a review of existing strategies in the feature extraction and pattern recognition stages of gait recognition
Summary
Much research effort has been devoted to the study of vision-based gait recognition. Zhang et al (2004) employed a two-dimensional five-link body model to represent the walking pattern They extracted the gait features from image sequences using Metropolis-Hasting method. Though model-based approaches are more robust to view and scale variations, accurately locate the joints positions is a strenuous task due to the non-rigid structures of the human body and to self-occlusion (Yang et al, 2008; Wang et al, 2011). For this reason, researchers often turn to model-free approaches. The gait representation obtained by model-free approaches can be broadly categorized into appearancebased representation, transformation-based representation and distribution-based representation
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