Abstract

The proliferation of innovative recognition and surveillance systems encompasses several algorithms and techniques. Human gait based recognition system, a subset of behaviour-based biometrics such as style, skills, language designs, support recognition and surveillance. In this manuscript, we propose to classify human gait as normal gait or abnormal gait through silhouette reconstruction and ellipse-fitting model. In ellipse-fitting, this article proposes a novel method of expanding the bounding box by covering human gait from head to toe for providing robust information about an individual as compared to the contemporary methodologies that acquire gait features from head to ankle. The other objective of this study is emphasizing the requirement of storing abnormal human gaits in databases for efficient recognition and surveillance, thereby improving the processing time coupled with the recognition rate. The Sparse Representation classifier (SRC) automatically classifies the normal and abnormal gait based on the characteristic vectors. The solicitation of 105 subjects facilitated the definition of the test sample. A camera with lower resolution recorded each subjects’ images of normal and abnormal walking pattern. An extensive iterative process determined that the proposed method achieved 99.52 % classification accuracy, using SRC in contrast to the state of the art methodologies and basic classifiers.

Keywords:

Ellipse-fitting, elliptical feature extraction, features vectors, gait classification, silhouette reconstruction, sparse representation classifier,

References

Competing interests

The authors have no competing interests.

Open Access Policy

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Copyright

The authors have no competing interests.