A time-frequency classifier for human gait recognition

Bijan G. Mobasseri; Moeness G. Amin

doi:10.1117/12.819060

5 May 2009 A time-frequency classifier for human gait recognition

Bijan G. Mobasseri, Moeness G. Amin

Proceedings Volume 7306, Optics and Photonics in Global Homeland Security V and Biometric Technology for Human Identification VI; 730628 (2009) https://doi.org/10.1117/12.819060
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

Radar has established itself as an effective all-weather, day or night sensor. Radar signals can penetrate walls and provide information on moving targets. Recently, radar has been used as an effective biometric sensor for classification of gait. The return from a coherent radar system contains a frequency offset in the carrier frequency, known as the Doppler Effect. The movements of arms and legs give rise to micro Doppler which can be clearly detailed in the time-frequency domain using traditional or modern time-frequency signal representation. In this paper we propose a gait classifier based on subspace learning using principal components analysis(PCA). The training set consists of feature vectors defined as either time or frequency snapshots taken from the spectrogram of radar backscatter. We show that gait signature is captured effectively in feature vectors. Feature vectors are then used in training a minimum distance classifier based on Mahalanobis distance metric. Results show that gait classification with high accuracy and short observation window is achievable using the proposed classifier.

Citation Download Citation

Bijan G. Mobasseri and Moeness G. Amin "A time-frequency classifier for human gait recognition", Proc. SPIE 7306, Optics and Photonics in Global Homeland Security V and Biometric Technology for Human Identification VI, 730628 (5 May 2009); https://doi.org/10.1117/12.819060

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