Human Gait Recognition Research Articles

Gait Recognition Based on Silhouettes Sequences and Neural Networks for Human Identification

Human gait recognition is moving ahead by the need for automated person identification and verification at a distance in many applications. In this paper our system presented silhouette videos and neural networks based system for human identification. For each sequence of silhouette images, an automated Region of Interest (ROI) algorithm applied to reduce dimensionality, extract gait features has been attempted with 3level 2Dimension Discrete Wavelet Transform (3L-2D-DWT), edges detection and gait cycle were used to extract relevant feature. Back propagations neural networks used as pattern recognition. The MATLAB software graphics user interface is designed to display result and simplify the use of the system. The developed system has been evaluated using (TUM-IITKGP) which contains three different type of walking categories and the results demonstrate that the proposed system achieves 98.8%, 95.87% and 88% for normal walk, walking with backpack and walking with hand in pocket category respectively of correct recognition. We concluded that the different side of view movement increase reliability of the key extracted feature and improve the neural network performance which opens a scope for further development.

Gait Recognition Based on Silhouettes Sequences and Neural Networks for Human Identification

Human gait recognition is moving ahead by the need for automated person identification and verification at a distance in many applications. In this paper our system presented silhouette videos and neural networks based system for human identification. For each sequence of silhouette images, an automated Region of Interest (ROI) algorithm applied to reduce dimensionality, extract gait features has been attempted with 3level 2Dimension Discrete Wavelet Transform (3L-2D-DWT), edges detection and gait cycle were used to extract relevant feature. Back propagations neural networks used as pattern recognition. The MATLAB software graphics user interface is designed to display result and simplify the use of the system. The developed system has been evaluated using (TUM-IITKGP) which contains three different type of walking categories and the results demonstrate that the proposed system achieves 98.8%, 95.87% and 88% for normal walk, walking with backpack and walking with hand in pocket category respectively of correct recognition. We concluded that the different side of view movement increase reliability of the key extracted feature and improve the neural network performance which opens a scope for further development.

Application and research of MEMS sensor in gait recognition algorithm

In recent years, research on gait recognition, identity recognition and gait certification has attracted increasing attention. With the development and maturity of MEMS technology, human gait recognition based on MEMS sensor has been widely used. On this basis, this paper studied the application of MEMS sensor in gait recognition algorithm. Firstly, the development trend of gait recognition and the development status of MEMS were briefly introduced. Secondly, we implemented feature selection through MEMS sensor, and proposed a posture recognition algorithm, a step counting algorithm and a fall-down algorithm. Finally, the algorithms were tested to draw the following conclusions: The proposed algorithm can effectively monitor and identify gait of human body. When weightlessness feature is added, the sensitivity of the test remains, and the specificity increases. To sum up, the algorithm designed in this paper has validity.

Human gait recognition using GEI-based local multi-scale feature descriptors

Human gait recognition is a biometric technique for persons identification based on their walking manner. This paper proposes a novel gait recognition approach capable of selecting information characteristics for human identification under different conditions including normal walking, carrying a bag and wearing a clothing for different angles of view; thereby enhancing the recognition accomplishment. The proposed approach relies on two feature extraction methods based on multi-scale feature descriptors including Multi-scale Local Binary Pattern (MLBP) and Gabor filter bank, through Spectra Regression Kernel Discriminant Analysis (SRKDA) reduction algorithm. The proposed features are extracted locally from two Region of Interest (ROIs) representing the dynamic areas in the Gait Energy Image (GEI). The experiments conducted on CASIA and USF Gait databases have shown that the suggested methods achieve better recognition performances up to 92% in terms of identification rate at rank-1 than the existing similar and recent state-of-the-art methods.

Human gait recognition based on Caffe deep learning framework

Human gait recognition based on Caffe deep learning framework

Recognition of human gait in oblique and frontal views using Kinect

This study describes the recognition of human gait in the oblique and frontal views using novel gait features derived from the skeleton joints provided by Kinect. In D-joint, the skeleton joints were extracted directly from the Kinect, which generates the gait feature. On the other hand, H-joint distance is a feature of distance between the hip joint with other skeleton joints. Prior to the gait feature extraction, the skeleton joints provided by Kinect were pre-processed in order to standardize the size of the skeleton image as well as to detect the gait feature within a full gait cycle. To classify gait patterns according to its own group, a multi-layer perceptron was employed in the pattern recognition stage. Results show that a perfect recognition of human gait (100%) was attained for the frontal view using the feature of H-joint distance at the optimal multi-layer perceptron (20 hidden units).

GSI: efficient spatio-temporal template for human gait recognition

Human gait recognition is a challenging task in computer vision community. In order to represent the gait, the most common feature is a gait template. Many efficient templates have been developed recently, however, the effectiveness of the proposed motion models is still under investigation. A novel template feature, named gait salient image (GSI) is introduced in this paper. The main contribution of the proposed GSI is encoding the motion energy of gait into a single template. This idea is being conceptualised by applying appropriate spatio-temporal filter for extracting motion features and averaging it over a gait period. To show how GSI-based feature is being efficient, the proposed template is classified using PCA+LDA. Extensive experiments on popular gait databases reveal an improvement over the available methods in terms of efficiency and accuracy. The value of recognition rate is 58.44% for Rank1 and 76.60% for Rank5 based on the USF database.

Vision-Based Gait Recognition: A Survey

In the digital world of today, global security issues have given rise to video surveillance devices. Gait-based human recognition is an emerging behavioral biometric trait for intelligent surveillance monitoring because of its non-contact and non-cooperation with subjects. Other benefits of gait recognition in video surveillance are that it can be acquired at a distance and help to identify an object under low-resolution videos. This paper surveys extensively the current progress made towards vision-based human gait recognition. This paper discusses historical research that performs analysis of gait locomotion and provides information on how gait recognition can be performed. This paper describes measuring metrics that can be used to measure the performance of gait recognition model under verification and identification mode. This paper also provides an up-to-date review of existing studies on gait recognition representations (model based and model free). We also provide an extensive survey of available gait databases used in state-of-art gait recognition models, created since 1998. Furthermore, it offers insight into open research problems that help researchers to explore unripe areas in gait analysis, such as occlusion, view variations, and appearance changes in gait recognition. This paper also identifies the future perspectives in gait recognition and also outlines the proposed work.

Gait-based human recognition using partial wavelet coherence and phase features

In this paper, a multi-view human gait recognition method which utilizes Partial Wavelet Coherence (PWC) as a novel feature is proposed. The Euclidean distance representation of PWC of the 1D signals generated due to movements of hands, legs, shoulders from multi-view gait sequences preserves the spatio-temporal information of walking individual. This method directly extracts the dynamic information without using any model. We got 73.26% average recognition accuracy when considered only PWC feature. Further, we investigate Phase Feature (PF) which also preserves discriminant information of dynamic phase angle between body parts. When PF considered additionally with PWC feature the system performance improved significantly and 82.52% average recognition accuracy reported.

Support Vector Machine vs Pattern Network for Gait Recognition

Human recognition based on biometric information is important due to its reliability in identity verification. Gait recognition has ability to recognize individuals from a distance. This study includes human gait recognition based firstly on support vector machine (SVM) and secondly on PatternNet neural network. Experiments were conducted with comparisons based on the two approaches. Experimental results showed that the PattenNet neural network is more effective than the SVM in gait recognition.

A Human Body Gait Recognition System Based on Fourier Transform and Quartile Difference Extraction

A research method of gait recognition based on feature combination is proposed, which includes the wavelet transform (WT), Fourier transform (FT) and the quartile difference, to explore the gait recognition system in Internet of things human body sensor based on smart phone. It can extract the low-dimensional gait parameters of the acceleration signal, and these gait parameters can reflect the movement characteristics. Then, they are combined into feature vector to identify by a pattern recognition algorithm.Finally, the experimental verification is carried out in the experimental system. The results show thatthis gait recognition method simplifies the processing flow to a certain extent, and improves the recognition accuracy. In conclusion, the algorithm has a good effect, and can identify the gait behavior with high precision.

Clothing-invariant human gait recognition using an adaptive outlier detection method

Human gait as a behavioral biometric identifier has received much attention in recent years. But there are some challenges which hinder using this biometric in real applications. One of these challenges is clothing variations which complicates the recognition process. In this paper, we propose an adaptive outlier detection method to remove the effect of clothing on silhouettes. The proposed method detects the most similar parts of probe and each gallery sample independently and uses these parts to obtain a similarity measure. Towards this end, the distances of the probe and a gallery sample are calculated row by row which are then used to obtain an adaptive threshold to determine valid and invalid rows. The average distance per valid rows is then considered as dissimilarity measure of samples. Experimental results on OU-ISIR Gait Database, the Treadmill Dataset B and CASIA Gait Database, Dataset B, show that this method efficiently detects and removes the clothing effect on silhouettes and reaches about 82 and 84% successful recognition respectively.

Reducing covariate factors of gait recognition using feature selection and dictionary-based sparse coding

Human gait recognition is a behavioral biometrics method that aims to determine the identity of individuals through the manner and style of their distinctive walk. It is still a very challenging problem because natural human gait is affected by many covariate factors such as changes in the clothing, variations in viewing angle, and changes in carrying condition. This paper evaluates the most important features of gait under the carrying and clothing conditions. We find that the intra-class variations of the features that remain static during the gait cycle affect the recognition accuracy adversely. Thus, we introduce an effective and robust feature selection method based on the gait energy image. The new gait representation is less sensitive to these covariate factors. We also propose an augmentation technique to overcome some of the problems associated with the intra-class gait fluctuations, as well as if the amount of the training data is relatively small. Finally, we use dictionary learning with sparse coding and linear discriminant analysis to seek the best discriminative data representation before feeding it to the Nearest Centroid classifier. When our method is applied on the large CASIA-B gait data set, we are able to outperform existing gait methods by achieving the highest average result.

Subband entropy-based features for clothing invariant human gait recognition

This paper presents a wavelet-based feature extraction method for human gait recognition. The selection of features with most discriminative information is the key to improve recognition performance. The frequency domain representation of the gait image is obtained by using fast Fourier transforms. Next, a discrete wavelet transform is applied to the obtained spectrum. With single-level wavelet decomposition, four coefficients are generated. The sum of the entropy of these four wavelet coefficients is computed yielding the wavelet Entropy Image (wEnI) which is used here as the potential feature for human gait recognition. A template matching-based approach is used as the classification. The performance of the proposed wEnI feature is evaluated using whole-based and part-based methods. The experimental results show that the wEnI feature performs better compared to state-of-the-art gait features in common use.

Human gait recognition based on Haralick features

This paper proposes a supervised feature extraction approach that is capable of selecting distinctive features for the recognition of human gait under clothing and carrying conditions, thus improving the recognition performances. The principle of the suggested approach is based on the Haralick features extracted from gait energy image (GEI). These features are extracted locally by dividing vertically or horizontally the GEI locally into two or three equal regions of interest, respectively. RELIEF feature selection algorithm is then employed on the extracted features in order to select only the most relevant features with a minimum redundancy. The proposed method is evaluated on CASIA gait database (Dataset B) under variations of clothing and carrying conditions for different viewing angles, and the experimental results using k-NN classifier have yielded attractive results of up to 80% in terms of highest identification rate at rank-1 when compared to existing and similar state-of-the-art methods.

Multilinear Laplacian discriminant analysis for gait recognition

The extraction of discriminative features in presence of covariates for robust human gait recognition is a challenging task. The effect of covariate can be modelled as unknown feature contamination problem resulting in the conversion of useful or relevant feature into irrelevant one. This study presents new gait representation and recognition technique. The new technique represents gait features based on the Gabor function and discrete cosine transform of binary silhouettes. It is called as Gabor cosine feature, which represents binary gait video sequence as third order tensor. The discrimination capability of the extracted gait features has been enhanced using a new multilinear Laplacian discriminant analysis (MLDA). MLDA exploits benefit of Laplacian weighted scatter difference instead of simple scatter difference, generalised Rayleigh quotient as a class separability measure. The feasibility and performance of the proposed scheme has been evaluated using USF, CASIA, OU‐ISIR dataset. The experimental results show competitive performance in comparison with conventional gait recognition schemes.

Human gait recognition from motion capture data in signature poses

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 classiers, 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 classier. 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.

Frontal gait recognition based on spatio-temporal interest points

This paper proposes a spatio-temporal interest point (STIP)-based human gait recognition method for frontal gait videos. The proposed method extracts histograms of oriented gradient (HOG) for STIPs directly from gait videos without involving a gait cycle segment, which is required by silhouette analysis as used by most gait recognition methods. Moreover, silhouette extraction may be affected by noise and carried objects leading to consequent recognition error. Matches of STIPs between two gait videos are then found in terms of HOG to measure the similarity of two videos so as to achieve the goal of gait recognition. The experimental results offer evidence that our method outperforms the existing methods on the Carnegie-Mellon University Motion of Body database and the Chinese Academy of Sciences Institute of Automation data-set B.

Classification of Normal and Pathological Gait in Young Children Based on Foot Pressure Data.

Human gait recognition, an active research topic in computer vision, is generally based on data obtained from images/videos. We applied computer vision technology to classify pathology-related changes in gait in young children using a foot-pressure database collected using the GAITRite walkway system. As foot positioning changes with children's development, we also investigated the possibility of age estimation based on this data. Our results demonstrate that the data collected by the GAITRite system can be used for normal/pathological gait classification. Combining age information and normal/pathological gait classification increases the accuracy of the classifier. This novel approach could support the development of an accurate, real-time, and economic measure of gait abnormalities in children, able to provide important feedback to clinicians regarding the effect of rehabilitation interventions, and to support targeted treatment modifications.

Human gait recognition based on deterministic learning through multiple views fusion

Gait characteristics extracted from one single camera are limited and not comprehensive enough to develop a robust recognition system. This paper proposes a robust gait recognition method using multiple views fusion and deterministic learning. First, a multiple-views fusion strategy is introduced, in which gaits collected under different views are synthesized as a kind of synthesized silhouette images. Second, the synthesized silhouettes are characterized with four kinds of time-varying gait features, including three width features of the silhouette and one silhouette area feature. Third, gait variability underlying different individuals’ time-varying gait features is effectively modeled by using deterministic learning algorithm. This kind of variability reflects the change of synthesized silhouettes while preserving temporal dynamics information of human walking. Gait patterns are represented as the gait variability underlying time-varying gait features and a rapid recognition scheme is presented in published gait databases. Experimental results show that encouraging recognition accuracy can be achieved.