Automatic Gait Recognition Research Articles

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, empowering the automatic gait recognition field. Whereas gait recognition works usually focus on improving end-to-end performance measures, 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. Assessments have shown it is hard to distinguish individuals based only on their walking patterns in a subject-based identification scenario. In this setup, 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 6 and 10 Hz are more significant for learning these patterns than events and other biomechanical movements related to the gait cycle, as usually explored in the literature.

Performance Analysis of ANN based Gait Recognition

It is well-known that biometrics are a powerful tool for reliable automated person identification. Automatic gait recognition is one of the newest of the emergent biometrics and has many advantages over other biometrics. The most notable advantage is that it does not require contact with the subjects nor does it require the subject to be near a camera. This work employs a gait recognition process with binary silhouette-based input images and Artificial Neural Network (ANN) based classification in MATLAB. The performance of the recognition method depends significantly on the quality of the extracted binary silhouettes. In this work, a computationally low-cost fuzzy correlogram based method is employed for background subtraction. Even highly robust background subtraction and shadow elimination algorithms produce erroneous outputs at times with missing body portions, which consequently affect the recognition performance. Frame Difference Energy Image (FDEI) reconstruction is performed to alleviate the detrimental effect of improperly extracted silhouettes and to make the recognition method robust to partial incompleteness. Subsequently, features are extracted via two methods and fed to the BPNN (Back Propagation Neural Network based classifier which uses feature vector (exemplars) to compute similarity scores and carry out identification using weight vectors i.e. Frame-to-Exemplar-Distance (FED) vector. The FED uses the distance measure between pre-determined feature vectors and the weight vectors of the current frame as an identification criterion. The ANN performance is evaluated for recognition and speed parameters at different training gait angles.

Robust gait identification using Kinect dynamic skeleton data

Gait has been recently proposed as a biometric feature that, with respect to other human characteristics, can be captured at a distance without requiring the collaboration of the observed subject. Therefore, it turns out to be a promising approach for people identification in several scenarios, e.g. access control and forensic applications. In this paper, we propose an automatic gait recognition system based on a set of features acquired using the 3D skeletal tracking provided by the popular Kinect sensor. Gait features are defined in terms of distances between selected sets of joints and their vertical and lateral sway with respect to walking direction. Moreover we do not rely on any geometrical assumptions on the position of the sensor. The effectiveness of the defined gait features is shown in the case of person identification based on supervised classification, using the principal component analysis and the support vector machine. A rich set of experiments is provided in two scenarios: a controlled identification setup and a classical video-surveillance setting, respectively. Moreover, we investigate if gait can be considered invariant over time for an individual, at least in a time interval of few years, by comparing gait samples of several subjects three years apart. Our experimental analysis shows that the proposed method is robust to acquisition settings and achieves very competitive identification accuracy with respect to the state of the art.

Automatic Gait Recognition and its Potential Role in Counterterrorism

ABSTRACTClosed-circuit television footage can be used to assemble an often-complex picture of an incident and aid in the identification of suspects after a crime or terrorist attack has occurred. For example, such footage allowed the police to not only identify the 7/7 London bombers but also to piece together the details of the bombers' movements prior to the attack. In the case of the London bombers little attempt was made to disguise their identities but where such identities are concealed it is possible to identify suspects based on other unique biometric characteristics such as the style of walk, referred to as gait. Gait feature–based individual identification has received increased attention from biometrics researchers. In this article, we propose a novel gait biometric methodology that could contribute to the counterterrorism effort and the identification of individuals involved in crime.

Automatic Human Gait Imitation and Recognition in 3D from Monocular Video with an Uncalibrated Camera

A framework of imitating real human gait in 3D from monocular video of an uncalibrated camera directly and automatically is proposed. It firstly combines polygon‐approximation with deformable template‐matching, using knowledge of human anatomy to achieve the characteristics including static and dynamic parameters of real human gait. Then, these characteristics are processed in regularization and normalization. Finally, they are imposed on a 3D human motion model with prior constrains and universal gait knowledge to realize imitating human gait. In recognition based on this human gait imitation, firstly, the dimensionality of time‐sequences corresponding to motion curves is reduced by NPE. Then, we use the essential features acquired from human gait imitation as input and integrate HCRF with SVM as a whole classifier, realizing identification recognition on human gait. In associated experiment, this imitation framework is robust for the object’s clothes and backpacks to a certain extent. It does not need any manual assist and any camera model information. And it is fitting for straight indoors and the viewing angle for target is between 60° and 120°. In recognition testing, this kind of integrated classifier HCRF/SVM has comparatively higher recognition rate than the sole HCRF, SVM and typical baseline method.

Introduction and application of an automatic gait recognition method to diagnose movement disorders that arose of similar causes

Nowadays, researchers try to introduce a more convenient and fast approach (especially a non-invasive one) to diagnose diseases fast and more precisely. Some patient death may be because of wrong diagnosis. Some patients that suffer diseases such as Parkinson disease are known to be dead of wrong diagnosis. Such an approach would help physicians to focus on the correct disease and its treatment and to avoid wasting precious time – that may be critical for the patient – on diagnosis. In this study, we try to develop a new automated approach for classifying (diagnosing) locomotive patients using features that may be extracted from their gait signal. We selected four groups: patients with Huntington’s disease, Parkinson’s disease and Amyotrophic Lateral Sclerosis and a group of healthy subjects. Examining different available classifiers on all proposed features, we have introduced a novel feature with acceptably low error rate using quadratic Bayes classifier.

Automatic gait recognition based on probabilistic approach

AbstractA simple probabilistic method for online video based human identification is introduced in this article. The proposed method is based on a modified version of Motion Silhouette images (MSI) and recursive probability accumulation. The modified version of MSI is named the Moving Motion Silhouette Image (MMSI). Identification probability is accumulated recursively in a Bayesian framework to draw a single conclusion from the whole gait sequence. The probability is named the accumulated posterior probability (APP) and denotes the probability based on all the information available up to now. The proposed method is tested on the well‐known publicly available NLPR and SOTON gait databases. The experimental results demonstrate the effectiveness of the proposed algorithm and indicate the fact that using MMSI and APP for information fusion yields higher recognition rates as compared to previous gait recognition systems. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 400–408, 2010

Micro-Doppler-based target detection and feature extraction in indoor and outdoor environments

Abstract In many cases, a target or a structure on a target may have micro-motions, such as vibrations or rotations. Micro-motions of structures on a target may introduce frequency modulation on the returned radar signal and generate sidebands on the Doppler frequency shift of the target's body. The modulation due to micro-motion is called the micro-Doppler (m-D) phenomenon. In this paper, we present an effective quadratic time–frequency S-method-based approach in conjunction with the Viterbi algorithm to extract m-D features. For target recognition applications, mainly those in military surveillance and reconnaissance operations, m-D features have to be extracted quickly so that they can be used for real-time target identification. The S-method is computationally simple, requiring only slight modifications to the existing Fourier transform-based algorithm. The effectiveness of the S-method in extracting m-D features is demonstrated through the application to indoor and outdoor experimental data sets such as rotating fan and human gait. The Viterbi algorithm for the instantaneous frequency estimation is used to enhance the weak human m-D features in relatively high noise environments. As such, this paper contributes additional experimental m-D data and analysis, which should help in developing a better picture of the human gait m-D research and its applications to indoor and outdoor imaging and automatic gait recognition systems.

Comprehensive framework to gait recognition

Human gait is a spatio-temporal phenomenon and typifies the motion characteristics of an individual. The gait of a person is easily recognisable when extracted from a side view of the person. Accordingly, gait-recognition algorithms work best when presented with images where the person walks parallel to the camera (i.e. the image plane). A set of stances or key frames that occur during the walk cycle of an individual is chosen. This paper presents a novel approach adopted in automatic gait recognition in which the silhouette extracted is represented using Shannon entropy and extracts the height of the subject and periodicity of the gait. To classify unknown gait, they need to match the nearest neighbour in the stored database of extracted gait features, and the proposed approach are tested on the data sets and is found to be quite satisfactory in natural walk conditions. In addition, the proposed decision fusion enables the performance improvement by integrating multiple ones with different confidence measures.

Automatic gait recognition based on statistical shape analysis

Gait recognition has recently gained significant attention from computer vision researchers. This interest is strongly motivated by the need for automated person identification systems at a distance in visual surveillance and monitoring applications. The paper proposes a simple and efficient automatic gait recognition algorithm using statistical shape analysis. For each image sequence, an improved background subtraction procedure is used to extract moving silhouettes of a walking figure from the background. Temporal changes of the detected silhouettes are then represented as an associated sequence of complex vector configurations in a common coordinate frame, and are further analyzed using the Procrustes shape analysis method to obtain mean shape as gait signature. Supervised pattern classification techniques, based on the full Procrustes distance measure, are adopted for recognition. This method does not directly analyze the dynamics of gait, but implicitly uses the action of walking to capture the structural characteristics of gait, especially the shape cues of body biometrics. The algorithm is tested on a database consisting of 240 sequences from 20 different subjects walking at 3 viewing angles in an outdoor environment. Experimental results are included to demonstrate the encouraging performance of the proposed algorithm.

Automatic gait recognition using area-based metrics

A novel technique for analysing moving shapes is presented in an example application to automatic gait recognition. The technique uses masking functions to measure area as a time varying signal from a sequence of silhouettes of a walking subject. Essentially, this combines the simplicity of a baseline area measure with the specificity of the selected (masked) area. The dynamic temporal signal is used as a signature for automatic gait recognition. The approach is tested on the largest extant gait database, consisting of 114 subjects (filmed under laboratory conditions). Though individual masks have limited discriminatory ability, a correct classification rate of over 75% was achieved by combining information from different area masks. Knowledge of the leg with which the subject starts a gait cycle is shown to improve the recognition rate from individual masks, but has little influence on the recognition rate achieved from combining masks. Finally, this technique is used to attempt to discriminate between male and female subjects. The technique is presented in basic form: future work can improve implementation factors such as using better data fusion and classifiers with potential to increase discriminatory capability.

Automatic gait recognition by symmetry analysis

We describe a new method for automatic gait recognition based on analysing the symmetry of human motion using the Generalised Symmetry Operator. This approach is reinforced by the psychologists’ view that gait is a symmetrical pattern of motion and results show that gait can indeed be recognised by symmetry analysis.

Automatic gait recognition via statistical approaches for extended template features

A gait recognition system using extended template features is presented. A proposed statistical approach is applied for feature extraction from spatial and temporal templates. This method can be used to reduce data dimensionality and to optimize the class separability of different gait sequences simultaneously. Dimensionality reduction is achieved by template extraction followed by principal component analysis. Gait recognition is achieved in the canonical space using a measure of accumulated distance as the metric. By incorporating spatial and temporal information into an extended feature, gait recognition becomes more robust and accurate than using spatial or temporal features alone.

Human gait recognition in canonical space using temporal templates

A system for automatic gait recognition without segmentation of particular body parts is described. Eigenspace transformation (EST) has already proved useful for several tasks including face recognition, gait analysis, etc; it is optimal in dimensionality reduction by maximising the total scatter of all classes but is not optimal for class separability. A statistical approach that combines EST with canonical space transformation (CST) is proposed for gait recognition using temporal templates from a gait sequence as features. This method can be used to reduce data dimensionality and to optimise the class separability of different gait sequences simultaneously. Incorporating temporal information from optical-flow changes between two consecutive spatial templates, each temporal template extracted from computation of optical flow is projected from a high-dimensional image space to a single point in a low-dimensional canonical space. Using template matching, recognition of human gait becomes much faster and simpler in this new space. As such, the combination of EST and CST is shown to be of considerable potential in an emerging new biometric.