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Related Experiment Videos

Identification of humans using gait.

Amit Kale1, Aravind Sundaresan, A N Rajagopalan

  • 1Department of Computer Science, University of Kentucky, Lexington, KY 40506, USA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|September 29, 2004
PubMed
Summary

We present a novel view-based method for human gait recognition using silhouette features. This approach utilizes hidden Markov models (HMMs) for robust identification based on walking patterns.

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Area of Science:

  • Computer Vision
  • Biometrics
  • Pattern Recognition

Background:

  • Human recognition from gait is a challenging biometric task.
  • Existing methods often struggle with variations in viewpoint and data dimensionality.

Purpose of the Study:

  • To develop a robust view-based approach for human identification using gait.
  • To explore two distinct methods for gait feature extraction and representation.

Main Methods:

  • Utilized binarized silhouette features (width and full silhouette).
  • Employed two approaches: indirect (frame-to-exemplar distance) and direct feature vector analysis.
  • Applied hidden Markov models (HMMs) for gait representation and recognition.

Main Results:

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  • Both indirect and direct methods demonstrated effective gait recognition.
  • The HMM framework provided robustness in representation and identification.
  • Performance was validated across multiple public gait databases.

Conclusions:

  • The proposed view-based gait recognition methods are effective and robust.
  • HMMs offer a statistically sound approach for capturing gait dynamics.
  • The study highlights the potential of silhouette analysis for biometric identification.