Reliability and Generalization of Gait Biometrics Using 3D Inertial Sensor Data and 3D Optical System Trajectories

Particularities in the individuals’ style of walking have been explored for at least three decades as a biometric trait, fueling the automatic gait recognition field. Whereas, gait recognition works usually focus on improving end-to-end performance measures, and this work aims at understanding which individuals’ traces are more relevant to improve subjects’ separability. For such, a manifold projection technique and a multi-sensor gait dataset were adopted to investigate the impact of each data source characteristics on this separability. The assessments have shown it is hard to distinguish individuals based only on their walking patterns in a subject identification scenario. In this scenario, the subjects’ separability is more related to their physical characteristics than their movements related to gait cycles and biomechanical events. However, this study’s results also points to the feasibility of learning identity characteristics from individuals’ walking patterns learned from similarities and differences between subjects in a verification setup. The explorations concluded that periodic components occurring in frequencies between 6Hz and 10Hz are more significant for learning these patterns than events and other biomechanical movements related to the gait cycle, as usually explored in the literature.

Requirements for the Procedure for Assessing the Operator’s Person When Processing Accelerometer Data

The article considers the possibility of biometric authentication based on gait parameters, which are obtained after intelligent processing of the accelerometer data of a wearable device. The article discusses the main trends and trends in the field of biometric authentication, as well as authentication by gait parameters. The developed neural network algorithm and informative parameters are described in the authentication procedure based on the data of a single sensor of a portable device. The practical verification of the proposed approach is carried out on 32 subjects of different physiology. The results of the study show the possibility of distinguishing their own movements in 100% of cases, and the distinction of the subjects is more than 90%. Also, the final part of the article provides the requirements for the authentication procedure when processing accelerometric data of gait biometrics, the level of trust of the developed algorithm is determined.

A New Method for Gait Recognition Using 2D Poses

Over the last decades, biometrics has become an important way for human identification in many areas, since it can avoid frauds and increase the security of individuals in society. Nowadays, most popular biometric systems are based on fingerprint and face features. Despite the great development observed in Biometrics, an important challenge lasts, which is the automatic people identification in low-resolution videos captured in unconstrained scenarios, at a distance, in a covert and noninvasive way, with little or none subject cooperation. In these cases, gait biometrics can be the only choice. The goal of this work is to propose a new method for gait recognition using information extracted from 2D poses estimated over video sequences. For 2D pose estimation, our method uses OpenPose, an open-source robust pose estimator, capable of real-time multi-person detection and pose estimation with high accuracy and a good computational performance. In order to assess the new proposed method, we used two public gait datasets, CASIA Gait Dataset-A and CASIA Gait Dataset-B. Both datasets have videos of a number of people walking in different directions and conditions. In our new method, the classification is carried out by a 1-NN classifier. The best results were obtained by using the chi-square distance function, which obtained 95.00% of rank-1 recognition rate on CASIA Gait Dataset-A and 94.22% of rank-1 recognition rate on CASIA Gait Dataset-B, which are comparable to state-of-the-art results.

Kinect-based human gait identifcation under different covariate factors

Introduction: Nowadays human gait identification/recognition is available in a variety of applications due to rapid advances in biometrics technology. This makes them easier to use for security and surveillance. Due to the rise in terrorist attacks during the last ten years research has focused on the biometric traits in these applications and they are now capable of recognising human beings from a distance. The main reason for my research interest in Gait biometrics is because it is unobtrusive and requires lower image/video quality compared to other biometric traits. Materials and Methods: In this paper we propose investigating Kinect-based gait recognition using non-standard gait sequences. This study examines different scenarios to highlight the challenges of non-standard gait sequences. Gait signatures are extracted from the 20 joint points of the human body using a Microsoft Kinect sensor. Results and Discussion: This feature is constructed by calculating the distances between each two joint points from the 20 joint points of the human body provided which is known as the Euclidean Distance Feature (EDF). The experiments are based on five scenarios, and a Linear Discriminant Classifier (LDC) is used to test the performance of the proposed method. Conclusions: The results of the experiments indicate that the proposed method outperforms previous work in all scenarios.

On Using Gait Biometrics for Re-Identification in Automated Visual Surveillance

As surveillance becomes ubiquitous in such modern society due to the immense increase of crimes and the rise of terrorism activities, various government and military funded projects are devoted to research institutions to work on improving surveillance technology for the safety of their citizens. Because of the rapid growth of security cameras and impossibility of manpower to supervise them, the integration of biometric technologies into surveillance systems would be a critical factor for the automation of identity tracking over distributed cameras with disjoint views i.e. Re-Identification. The interest of using gait biometrics to re-identify people over networks of cameras emerges from the fact that the gait pattern can be captured and perceived at a distance as well as its non-invasive and less-intrusive nature.