Gait Classification of Twins and Non-Twins Siblings

This paper presents a classification analysis of gait biometric on twins and non-twins siblings. The aim of this paper is to investigate the existence or inexistence of similarity in the gait of twins and compare it to the gait of non-twins siblings. The motivation behind this paper is that a video-based surveillance system may not be able to rely on face biometric alone when dealing with twins. The features used are the angular displacement walking trajectories of lower limbs. Also this paper proposes a gait cycle normalization task via Bezier polynomial root-finding and re-sampling to ensure a robust analysis against differences in walking speed. Two established classifiers, the linear discriminant analysis (LDA) and k-nearest neighbor are used to classify the data sets of twins and non-twins siblings. Results may indicate that there is similarity in the gait of twins.