Exploring the use of the lower part of the legs for people identification

The integration of biometric technologies into surveillance systems is a major step milestone to improve the automation process in order to recognize criminal offenders and track them across different places. The suitability of gait biometrics for surveillance applications emerges from the fact that the walking pattern can be captured and perceived from a distance even with poor resolution video as opposed to other biometric modalities which their performance deteriorates in surveillance scenarios. In this research article, we explore the use of gait biometrics using features derived from the lower part of legs for people identification. The gait features are extracted from both spatial and temporal domains. These features are mainly estimated from the Shapes of the Gaps between the Lower Limbs (SGLLs) when walking using the Hu's Invariant Moments. The spatial features are those of a single SGLL extracted from one image and considered as local characteristics. Comparative analysis is conducted against well-established methods. The attained results confirm that people identification using gait features extracted from the lower limbs is still perceivable with better recognition rates even under the influence of different covariate factors.