Gait recognition based on shape and motion analysis of silhouette contours

This paper presents a three-phase gait recognition method that analyses the spatio-temporal shape and dynamic motion (STS-DM) characteristics of a human subject’s silhouettes to identify the subject in the presence of most of the challenging factors that affect existing gait recognition systems. In phase 1, phase-weighted magnitude spectra of the Fourier descriptor of the silhouette contours at ten phases of a gait period are used to analyse the spatio-temporal changes of the subject’s shape. A component-based Fourier descriptor based on anatomical studies of human body is used to achieve robustness against shape variations caused by all common types of small carrying conditions with folded hands, at the subject’s back and in upright position. In phase 2, a full-body shape and motion analysis is performed by fitting ellipses to contour segments of ten phases of a gait period and using a histogram matching with Bhattacharyya distance of parameters of the ellipses as dissimilarity scores. In phase 3, dynamic time warping is used to analyse the angular rotation pattern of the subject’s leading knee with a consideration of arm-swing over a gait period to achieve identification that is invariant to walking speed, limited clothing variations, hair style changes and shadows under feet. The match scores generated in the three phases are fused using weight-based score-level fusion for robust identification in the presence of missing and distorted frames, and occlusion in the scene. Experimental analyses on various publicly available data sets show that STS-DM outperforms several state-of-the-art gait recognition methods

Kinematic discrimination of ataxia in horses is facilitated by blindfolding

BACKGROUND: Agreement among experienced clinicians is poor when assessing the presence and severity of ataxia, especially when signs are mild. Consequently, objective gait measurements might be beneficial for assessment of horses with neurological diseases. OBJECTIVES: To assess diagnostic criteria using motion capture to measure variability in spatial gait-characteristics and swing duration derived from ataxic and non-ataxic horses, and to assess if variability increases with blindfolding. STUDY DESIGN: Cross-sectional. METHODS: A total of 21 horses underwent measurements in a gait laboratory and live neurological grading by multiple raters. In the gait laboratory, the horses were made to walk across a runway surrounded by a 12-camera motion capture system with a sample frequency of 240 Hz. They were made to walk normally and with a blindfold in at least three trials each. Displacements of reflective markers on head, fetlock, hoof, fourth lumbar vertebra, tuber coxae and sacrum derived from three to four consecutive strides were processed and descriptive statistics, receiver operator characteristics (ROC) to determine the diagnostic sensitivity, specificity and area under the curve (AUC), and correlation between median ataxia grade and gait parameters were determined. RESULTS: For horses with a median ataxia grade ≥2, coefficient of variation for the location of maximum vertical displacement of pelvic and thoracic distal limbs generated good diagnostic yield. The hoofs of the thoracic limbs yielded an AUC of 0.81 with 64% sensitivity and 90% specificity. Blindfolding exacerbated the variation for ataxic horses compared to non-ataxic horses with the hoof marker having an AUC of 0.89 with 82% sensitivity and 90% specificity. MAIN LIMITATIONS: The low number of consecutive strides per horse obtained with motion capture could decrease diagnostic utility. CONCLUSIONS: Motion capture can objectively aid the assessment of horses with ataxia. Furthermore, blindfolding increases variation in distal pelvic limb kinematics making it a useful clinical tool

Silhouette-based gait recognition using Procrustes shape analysis and elliptic Fourier descriptors

This paper presents a gait recognition method which combines spatio-temporal motion characteristics, statistical and physical parameters (referred to as STM-SPP) of a human subject for its classification by analysing shape of the subject's silhouette contours using Procrustes shape analysis (PSA) and elliptic Fourier descriptors (EFDs). STM-SPP uses spatio-temporal gait characteristics and physical parameters of human body to resolve similar dissimilarity scores between probe and gallery sequences obtained by PSA. A part-based shape analysis using EFDs is also introduced to achieve robustness against carrying conditions. The classification results by PSA and EFDs are combined, resolving tie in ranking using contour matching based on Hu moments. Experimental results show STM-SPP outperforms several silhouette-based gait recognition methods

Gait Recognition

Gait recognition has received increasing attention as a remote biometric identification technology, i.e. it can achieve identification at the long distance that few other identification technologies can work. It shows enormous potential to apply in the field of criminal investigation, medical treatment, identity recognition, human‐computer interaction and so on. In this chapter, we introduce the state‐of‐the‐art gait recognition techniques, which include 3D‐based and 2D‐based methods, in the first part. And considering the advantages of 3D‐based methods, their related datasets are introduced as well as our gait database with both 2D silhouette images and 3D joints information in the second part. Given our gait dataset, a human walking model and the corresponding static and dynamic feature extraction are presented, which are verified to be view‐invariant, in the third part. And some gait‐based applications are introduced

Forensic gait analysis — Morphometric assessment from surveillance footage

© 2019 Elsevier B.V. Following the technological rise of surveillance cameras and their subsequent proliferation in public places, the use of information gathered by such means for investigative and evaluative purposes sparked a large interest in the forensic community and within policing scenarios. In particular, it is suggested that analysis of the body, especially the assessment of gait characteristics, can provide useful information to aid the investigation. This paper discusses the influences upon gait to mitigate some of the limitations of surveillance footage, including those due to the varying anatomical differences between individuals. Furthermore, the differences between various techniques applied to assess gait are discussed, including biometric gait recognition, forensic gait analysis, tracking technology, and marker technology. This review article discusses the limitations of the current methods for assessment of gait; exposing gaps within the literature in regard to various influences impacting upon the gait cycle. Furthermore, it suggests a ‘morphometric’ technique to enhance the available procedures to potentially facilitate the development of standardised protocols with supporting statistics and database. This in turn will provide meaningful information to forensic investigation, intelligence-gathering processes, and potentially as an additional method of forensic evaluation of evidence

How useful is thematic analysis as an elicitation technique for analyzing video of human gait in forensic podiatry?

The aim of this study was to evaluate how useful thematic analysis is in the elicitation of observations of gait from a video recording. This was undertaken by providing a video recording of human gait to “novice” and “expert” podiatry students. The observations were explored using the qualitative tool of thematic analysis. The exploration of human gait using this technique gave a rich abundance of information and demonstrated that a basic level of experience or knowledge is required to provide a simple description of human gait. With more expertise came a richer description of observation of human gait by the “expert” group compared to basic observations by the “novice” group. Thematic analysis allows the use of language and the depth of the information to be evaluated when observing human gait from a video recording