[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

Feature Extraction and HMM-Based Classification of Gait Video Sequences 243
Fig. 10. Dependency between average CCR and number N of states for PCA and LLE
variants
4 Conclusion
The average values presented in Fig. 9 and Fig. 10 suggest that the non-linear
dimensionality reduction method (LLE) is superior to the linear technique (PCA)
in application for classification of gait sequences. Generally, first of all just video
and image data sets are supposed to exhibit low-dimensional manifold structures
[22].
Application of the HMM as classifier of gait sequences was successful,
particularly in view of the fact that the classification accuracy equal to 100 % was
obtained.
Conclusions drawn from experiments with video sequences taken from the
CASIA Gait Database will be helpful during the next stage of the research which
will be based on video material from the municipal surveillance system.
Future research will also explore the influence of the feature selection methods
on the accuracy of the gait-based identification process. The promising Invasive
Weed Optimization (IWO) metaheuristic will be adapted to the searching the
feature space for an adequate subset of features.
References
[1] Boyd, J.E., Little, J.J.: Biometric Gait Recognition. In: Tistarelli, M., Bigun, J.,
Grosso, E. (eds.) Advanced Studies in Biometrics. LNCS, vol. 3161, pp. 19–42.
Springer, Heidelberg (2005)
[2] Nixon, M.S., Tan, T.N., Chellappa, R.: Human Identification Based on Gait.
Springer (2006)