No abstract

Given the continuing advances in gait biometrics, it appears prudent to investigate the translation of these techniques for forensic use. We address the question as to the confidence that might be given between any two such measurements. We use the locations of ankle, knee, and hip to derive a measure of the match between walking subjects in image sequences. The Instantaneous Posture Match algorithm, using Harr templates, kinematics, and anthropomorphic knowledge is used to determine their location. This is demonstrated using real CCTV recorded at Gatwick International Airport, laboratory images from the multiview CASIA-B data set, and an example of real scene of crime video. To access the measurement confidence, we study the mean intra- and inter-match scores as a function of database size. These measures converge to constant and separate values, indicating that the match measure derived from individual comparisons is considerably smaller than the average match measure from a population.

Abstract-We present a new method for viewpoint independent gait biometrics. The system relies on a single camera, does not require camera calibration, and works with a wide range of camera views. This is achieved by a formulation where the gait is self-calibrating. These properties make the proposed method particularly suitable for identification by gait, where the advantages of completely unobtrusiveness, remoteness, and covertness of the biometric system preclude the availability of camera information and specific walking directions. The approach has been assessed for feature extraction and recognition capabilities on the SOTON gait database and then evaluated on a multiview database to establish recognition capability with respect to view invariance. Moreover, tests on the multiview CASIA-B database, composed of more than 2270 video sequences with 65 different subjects walking freely along different walking directions, have been performed. The obtained results show that human identification by gait can be achieved without any knowledge of internal or external camera parameters with a mean correct classification rate of 73.6% across all views using purely dynamic gait features. The performance of the proposed method is particularly encouraging for application in surveillance scenarios.Index Terms-Gait biometrics, human identification, view invariant.

In spite of the unprecedented popularity to use innovative gaming concepts within the educational context in order to promote active learning, engage people and solve motivational problems, there is an emerging body of research work arguing that gamification is not effective to increase neither the students engagement nor the learning outcomes. In this research paper, an empirical study is conducted to explore how gamification can firstly affect the student learning engagement and the interactivity level with e-learning technologies. Secondly, whether it can be considered as a driving thrust to support sustained learning. A question board is designed and implemented to enable students ask and answer questions related to their taught modules where academic staff can also contribute and validate the most correct answers. The acquisition of data is performed through a period of 10 months in order to investigate the gamification impact over time. The gamified platform was integrated with the online e-learning portal of a university where the adoption of e-learning is considered extremely poor. The obtained results have revealed that gamification can be considered as a valuable tool to entice users for the uptake of educational systems and increase their interactivity and engagement.

Abstract. Human motion analysis has received a great attention from researchers in the last decade due to its potential use in different applications. We propose a new approach to extract human joints (vertex positions) using a model-based method. Motion templates describing the motion of the joints as derived by gait analysis, are parametrized using the elliptic Fourier descriptors. The heel strike data is exploited to reduce the dimensionality of the parametric models. People walk normal to the viewing plane, as major gait information is available in a sagittal view. The ankle, knee and hip joints are successfully extracted with high accuracy for indoor and outdoor data. In this way, we have established a baseline analysis which can be deployed in recognition, marker-less analysis and other areas. The experimental results confirmed the robustness of the proposed method to recognize walking subjects with a correct classification rate of %92.