Human gait recognition from motion capture data in signature poses

Abstract

Most contribution to the field of structure-based human gait recognition has been done through design of extraordinary gait features. Many research groups that address this topic introduce a unique combination of gait features, select a couple of well-known object classifiers, and test some variations of their methods on their custom Kinect databases. For a practical system, it is not necessary to invent an ideal gait feature -there have been many good geometric features designed -but to smartly process the data there are at our disposal. This work proposes a gait recognition method without design of novel gait features; instead, we suggest an effective and highly efficient way of processing known types of features. Our method extracts a couple of joint angles from two signature poses within a gait cycle to form a gait pattern descriptor, and classifies the query subject by the baseline 1-NN classifier. Not only are these poses distinctive enough, they also rarely accommodate motion irregularities that would result in confusion of identities. We experimentally demonstrate that our gait recognition method outperforms other relevant methods in terms of recognition rate and computational complexity. Evaluations were performed on an experimental database that precisely simulates street-level video surveillance environment.

Electronic Skin Wearable Sensors for Detecting Lumbar–Pelvic Movements

“…The organic motion system is a similar mark-less vision system with a wide tracking range compared to Kinect [33]. KinaTrax is another powerful marker-less vision-based posture analysis system with multiple camera set-ups that can adequately cover an area size of a baseball court [35].…”

“…In general, the marker-less based systems are relatively cheaper and easier to setup than the marker-based systems [35]. However, they provide lower accuracy for real-time movement tracking.…”

Calibrated and synchronized multi-view video and motion capture dataset for evaluation of gait recognition

“…Since motion capture technology became recently more affordable, 3D structure-based gait recognition has attracted more interest from researchers [5,7,32,66]. Recently, a new benchmark data and evaluation protocols for moCap-based gait recognition have been proposed in [6].…”

“…However, until now, only limited research on 3D data-based biometric gait recognition has been done because of restricted availability of synchronized multi-view data with proper camera calibration [50]. Motivated by this, taking into account a forecast formulated at Stanford for an evolution of methods for the capture of human movement [52], promising results that were obtained using marker-less 3D motion tracking [39] and marker-based 3D motion tracking [5,32] for gait recognition, in this work we introduce a dataset for marker-less 3D motion tracking and 3D gait recognition. Since gait abnormalities are often impossible to detect by eye or with video based systems, we also share data from marker-based moCap, which is synchronized and calibrated with marker-less motion capture system.…”

Comparison of feature extraction and normalization methods for speaker recognition using grid-audiovisual database

“…Biometrics characteristics can be divided into two main types [1]: Behavioural and Physiological traits. Examples of the behavioral traits include Voice, Signature, Gait, and Keystroke [2][3][4][5], and [6]. On the other hand, Physiological traits include Iris, Retina, Face, Ear, DNA, Hand Geometry, Palm, and Fingerprint [7][8][9][10][11][12].…”