Extraction of bodily features for gait recognition and gait attractiveness evaluation

This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-012-1319-2. Copyright @ 2012 Springer.Although there has been much previous research on which bodily features are most important in gait analysis, the questions of which features should be extracted from gait, and why these features in particular should be extracted, have not been convincingly answered. The primary goal of the study reported here was to take an analytical approach to answering these questions, in the context of identifying the features that are most important for gait recognition and gait attractiveness evaluation. Using precise 3D gait motion data obtained from motion capture, we analyzed the relative motions from different body segments to a root marker (located on the lower back) of 30 males by the fixed root method, and compared them with the original motions without fixing root. Some particular features were obtained by principal component analysis (PCA). The left lower arm, lower legs and hips were identified as important features for gait recognition. For gait attractiveness evaluation, the lower legs were recognized as important features.Dorothy Hodgkin Postgraduate Award and HEFCE

Automatic recognition of gait patterns in human motor disorders using machine learning: A review

Background: automatic recognition of human movement is an effective strategy to assess abnormal gait patterns. Machine learning approaches are mainly applied due to their ability to work with multidimensional nonlinear features. Purpose: to compare several machine learning algorithms employed for gait pattern recognition in motor disorders using discriminant features extracted from gait dynamics. Additionally, this work highlights procedures that improve gait recognition performance. Methods: we conducted an electronic literature search on Web of Science, IEEE, and Scopus, using “human recognition”, “gait patterns’’, and “feature selection methods” as relevant keywords. Results: analysis of the literature showed that kernel principal component analysis and genetic algorithms are efficient at reducing dimensional features due to their ability to process nonlinear data and converge to global optimum. Comparative analysis of machine learning performance showed that support vector machines (SVMs) exhibited higher accuracy and proper generalization for new instances. Conclusions: automatic recognition by combining dimensional data reduction, cross-validation and normalization techniques with SVMs may offer an objective and rapid tool for investigating the subject's clinical status. Future directions comprise the real-time application of these tools to drive powered assistive devices in free-living conditions.This work was supported by the FCT - Fundação para a Ciência e Tecnologia - with the reference scholarship SFRH/BD/108309/2015, and the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalização (POCI) - with the reference project POCI-01-0145-FEDER-006941. Also, this work was partially supported by grant RYC-2014-16613 by Spanish Ministry of Economy and Competitiveness

A Review on Human Gait Detection

The human gait is the identification of human locomotive based on limbs position or action The tracking of human gait can help in various applications like normal and abnormal gait fall detection gender detection age detection biometrics and in some terrorist and criminal activity detection The present work carried out is a review of various methodologies employed in human gait detection The analysis describes that the different feature extraction and machine learning techniques to be adopted for the identification of human gait based on the purpose of the applicatio

Principal Component Analysis of Gait Kinematics Data in Acute and Chronic Stroke Patients

We present the joint angles analysis by means of the principal component analysis (PCA). The data from twenty-seven acute and chronic hemiplegic patients were used and compared with data from five healthy subjects. The data were collected during walking along a 10-meter long path. The PCA was applied on a data set consisting of hip, knee, and ankle joint angles of the paretic and the nonparetic leg. The results point to significant differences in joint synergies between the acute and chronic hemiplegic patients that are not revealed when applying typical methods for gait assessment (clinical scores, gait speed, and gait symmetry). The results suggest that the PCA allows classification of the origin for the deficit in the gait when compared to healthy subjects; hence, the most appropriate treatment can be applied in the rehabilitation

Sparse error gait image: a new representation for gait recognition

The performance of a gait recognition system is very much related to the usage of efficient feature representation and recognition modules. The first extracts features from an input image sequence to represent a user's distinctive gait pattern. The recognition module then compares the features of a probe user with those registered in the gallery database. This paper presents a novel gait feature representation, called Sparse Error Gait Image (SEGI), derived from the application of Robust Principal Component Analysis (RPCA) to Gait Energy Images (GEI). GEIs obtained from the same user at different instants always present some differences. Applying RPCA results in low-rank and sparse error components, the former capturing the commonalities and encompassing the small differences between input GEIs, while the larger differences are captured by the sparse error component. The proposed SEGI representation exploits the latter for recognition purposes. This paper also proposes two simple approaches for the recognition module, to exploit the SEGI, based on the computation of a Euclidean norm or the Euclidean distance. Using these simple recognition methods and the proposed SEGI representation gait recognition, results equivalent to the state-of-the-art are obtained

Gait Recognition: Databases, Representations, and Applications

There has been considerable progress in automatic recognition of people by the way they walk since its inception almost 20 years ago: there is now a plethora of technique and data which continue to show that a person’s walking is indeed unique. Gait recognition is a behavioural biometric which is available even at a distance from a camera when other biometrics may be occluded, obscured or suffering from insufficient image resolution (e.g. a blurred face image or a face image occluded by mask). Since gait recognition does not require subject cooperation due to its non-invasive capturing process, it is expected to be applied for criminal investigation from CCTV footages in public and private spaces. This article introduces current progress, a research background, and basic approaches for gait recognition in the first three sections, and two important aspects of gait recognition, the gait databases and gait feature representations are described in the following sections.Publicly available gait databases are essential for benchmarking individual approaches, and such databases should contain a sufficient number of subjects as well as covariate factors to realize statistically reliable performance evaluation and also robust gait recognition. Gait recognition researchers have therefore built such useful gait databases which incorporate subject diversities and/or rich covariate factors.Gait feature representation is also an important aspect for effective and efficient gait recognition. We describe the two main approaches to representation: model-free (appearance-based) approaches and model-based approaches. In particular, silhouette-based model-free approaches predominate in recent studies and many have been proposed and are described in detail.Performance evaluation results of such recent gait feature representations on two of the publicly available gait databases are reported: USF Human ID with rich covariate factors such as views, surface, bag, shoes, time elapse; and OU-ISIR LP with more than 4,000 subjects. Since gait recognition is suitable for criminal investigation applications of the gait recognition to forensics are addressed with real criminal cases in the application section. Finally, several open problems of the gait recognition are discussed to show future research avenues of the gait recognition

CDBMGCIG: Design of a Cross-Domain Bioinspired Model for identification of Gait Components via Iterated GANs

This Gait identification assists in recognition of human body components from temporal image sequences. Such components consist of connected-body entities including head, upper body, lower body regions. Existing Gait recognition models use deep learning methods including variants of Convolutional Neural Networks (CNNs), Q-Learning, etc. But these methods are either highly complex, or do not perform well under complex background conditions. Moreover, most of these models are validated on a specific environmental condition, and cannot be scaled for general-purpose deployments. To overcome these issues, this text proposes design of a novel cross-domain bioinspired model for identification of gait components via Iterated Generative Adversarial Networks (IGANs). The proposed model initially extracts multidomain pixel-level feature sets from different images. These include frequency components via Fourier analysis, entropy components via Cosine analysis, spatial components via Gabor analysis, and window-based components via Wavelet &Convolutional analysis. These feature sets are processed via a Grey Wolf Optimization (GWO) Model, which assists in identification of high-density & highly variant features for different gait components. These features are classified via an iterated GAN, which comprises of Generator & Discriminator ssModels that assist in evaluating connected body components. These operations generate component-level scores that assist in identification of gait from complex background images. Due to which, the proposed model was observed to achieve 9.5% higher accuracy, 3.4% higher precision, and 2.9% higher recall than existing gait identification methods. The model also uses iterative learning, due to which its accuracy is incrementally improved w.r.t. number of evaluated image sets