Frame–based classification for cross-speed gait recognition
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The use of human gait as the means of biometric identification has gained a lot of attention in the past few years, mostly due to its enormous potential. Such biometrics can be captured at public places from a distance without subjects collaboration, awareness and even consent. However, there are still numerous challenges caused by influence of covariate factors like changes of walking speed, view, clothing, footwear etc., that have negative impact on recognition performance. In this paper we tackle walking speed changes with a skeleton model-based gait recognition system focusing on improving algorithm robustness and improving the performance at higher walking speed changes. We achieve these by proposing frame based classification method, which overcomes the main shortcoming of distance based classification methods, which are very sensitive to gait cycle starting point detection. The proposed technique is starting point invariant with respect to gait cycle starts and as such ensures independence of classification from gait cycle start positions. Additionally, we propose wavelet transform based signal approximation, which enables the analysis of feature signals on different frequency space resolutions and diminishes the need for using feature transformation that require training. With the evaluation on OU-ISIR gait dataset we demonstrate state of the art performance of proposed methods.
KeywordsBiometric identification Computer vision Gait recognition Walking speed invariance
Research was partly financed by the European Union, European Social Fund. This research was supported in parts also by the ARRS (Slovenian Research Agency) Research Program P2-0214 (A) Computer Vision and the ARRS Research Program P2-0250 (B) Metrology and Biometric Systems.
Compliance with Ethical Standards
Conflict of interests
The authors declare that they have no conflict of interest.
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