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Feature selection based on a fuzzy complementary criterion: application to gait recognition using ground reaction

S P Moustakidis¹, J B Theocharis, G Giakas

¹Department of Electrical and Computer Engineering, Division of Electronics and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.

Computer Methods in Biomechanics and Biomedical Engineering

|May 21, 2011

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This study introduces an efficient wavelet-based feature selection method for subject recognition using ground reaction forces. The novel fuzzy complementary criterion (FuzCoC) ensures optimal feature selection for improved gait recognition accuracy.

Area of Science:

Biometrics
Signal Processing
Machine Learning

Background:

Gait recognition is crucial for subject identification.
Ground reaction force (GRF) measurements offer rich data for gait analysis.
Existing feature selection methods may not fully exploit complementary information in GRF data.

Purpose of the Study:

To propose an efficient wavelet-based feature selection (FS) method for subject recognition using GRF.
To introduce a fuzzy complementary criterion (FuzCoC) for iterative feature selection.
To develop novel techniques for enhanced feature extraction and selection from gait data.

Main Methods:

Wavelet packet (WP) decomposition of gait signals to identify discriminating frequency sub-bands.
Application of a computationally simple FS method guided by the FuzCoC criterion.

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Iterative selection of features that maximize additional information content.

Main Results:

The proposed method effectively selects powerful and complementary features from WP coefficients.
Comparative analysis demonstrates the superiority of the developed approach over existing methods in gait recognition.
Validation confirms the efficiency and accuracy of the wavelet-based FS for subject recognition.

Conclusions:

The novel FuzCoC-driven wavelet-based FS method provides an efficient and accurate approach for subject recognition using GRF.
The technique successfully extracts salient and complementary features, enhancing gait recognition performance.
This work contributes a valuable tool for biometric identification systems relying on gait analysis.