This thesis is based on four manuscripts where two of them were accepted and two were submitted to peer-reviewed journals. The experimental work behind the thesis was conducted at the Institute of Neuroscience and Pharmacology, University of Copenhagen. The purpose of the studies was to explore the variability of human gait and to conduct new methods for precise estimation of the kinematic parameters applied in forensic gait analysis. The gait studies were conducted in a custom built gait laboratory designed to obtain optimal conditions for markerless motion analysis. The set-up consisted of eight synchronised cameras located in the corners of the laboratory, which were connected to a single computer. The captured images were processed with stereovision-based algorithms to provide accurate 3D reconstructions of the participants. The 3D reconstructions of the participants were obtained during normal walking and the kinematics were extracted with manual and automatic methods. The kinematic results from the automatic approach were compared to marker-based motion capture to validate the precision. The results showed that the proposed markerless motion capture method had a precision comparable to marker-based methods in the frontal plane and the sagittal plane. Similar markerless motion capture methods could therefore provide the basis for reliable gait recognition based on kinematic parameters. The manual annotations were compared to the actual anthropometric measurements obtained from MRI scans and the intra- and inter-observer variability was also quantified to observe the associated effect on recognition. The results showed not only that the kinematics in the lower extremities were important but also that the kinematics in the shoulders had a high discriminatory power. Likewise, the shank length was also highly discriminatory, which has not been previously reported. However, it is important that the same expert performs all annotations, as the inter-observer variability was high compared to the variability between the participants. The MRI scans were also applied to estimate the errors of existing marker-based regression equations to predict the joint centres. The errors in the HJC and the AJC were surprisingly high, which may affect the computations of the joint kinetics and thus the understanding of gait dynamics. On the other hand, the effect on the kinematics would be limited and thus the existing regression equations provide a reliable basis to validate markerless motion capture methods as long as the limitations regarding STA and the placement of the markers are considered in the data interpretation. New regression equations corrected the estimated bias and they also accounted for the significant sex differences in pelvis.