scholarly journals Gait‐D: Skeleton‐based gait feature decomposition for gait recognition

2021 ◽  
Author(s):  
Shuo Gao ◽  
Jing Yun ◽  
Yumeng Zhao ◽  
Limin Liu
Keyword(s):  
Gait Recognition ◽  
Gait Feature

A Gait Feature Extraction Algorithm Based on Fusion of Spatial and Frequency Feature

2013 ◽  
Vol 291-294 ◽  
pp. 2492-2495
Author(s):  
Xiao Ke Zhu ◽  
Xiao Pan Chen ◽  
Fan Zhang
Keyword(s):  
Feature Extraction ◽  
Moving Objects ◽  
Gait Recognition ◽  
Morphological Processing ◽  
Moving Object ◽  
Classification Rate ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Gait Feature ◽  
Frequency Feature

In order to enhance the accuracy of gait recognition, a new gait feature extraction algorithm is proposed. Firstly, the gait images are preprocessed to extract moving objects, including background modeling, moving object extracting and morphological processing. Secondly, an equidistant slicing curve model based on system of polar coordinate is designed to slice the moving object, and the slicing vector is used to describe the spatial feature; Thirdly, the slicing vector is converted into frequency signal by Fourier transform to extract the frequency feature. Finally, the above two features are fused and used for the classification. The experimental results show that proposed algorithm provides higher correct classification rate than the algorithms using single feature, and meets the requirements of the real-time.

scholarly journals Gait Recognition Using GEI and AFDEI

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Jing Luo ◽  
Jianliang Zhang ◽  
Chunyuan Zi ◽  
Ying Niu ◽  
Huixin Tian ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Gait Recognition ◽  
Dynamic Information ◽  
Static Information ◽  
Gait Feature ◽  
Time Requirements ◽  
The Common ◽  
The Moment ◽  
Neighbor Classifier ◽  
Better Than

Gait energy image (GEI) preserves the dynamic and static information of a gait sequence. The common static information includes the appearance and shape of the human body and the dynamic information includes the variation of frequency and phase. However, there is no consideration of the time that normalizes each silhouette within the GEI. As regards this problem, this paper proposed the accumulated frame difference energy image (AFDEI), which can reflect the time characteristics. The fusion of the moment invariants extracted from GEI and AFDEI was selected as the gait feature. Then, gait recognition was accomplished using the nearest neighbor classifier based on the Euclidean distance. Finally, to verify the performance, the proposed algorithm was compared with the GEI + 2D-PCA and SFDEI + HMM on the CASIA-B gait database. The experimental results have shown that the proposed algorithm performs better than GEI + 2D-PCA and SFDEI + HMM and meets the real-time requirements.

scholarly journals Gait Energy Response Functions for Gait Recognition against Various Clothing and Carrying Status

2018 ◽  
Vol 8 (8) ◽  
pp. 1380 ◽  
Author(s):  
Xiang Li ◽  
Yasushi Makihara ◽  
Chi Xu ◽  
Daigo Muramatsu ◽  
Yasushi Yagi ◽  
...  
Keyword(s):  
State Of The Art ◽  
Gait Recognition ◽  
Metric Learning ◽  
Lookup Table ◽  
Body Parts ◽  
Energy Response ◽  
Gabor Filtering ◽  
Discrimination Capability ◽  
Gait Feature ◽  
Processing Techniques

Silhouette-based gait representations are widely used in the current gait recognition community due to their effectiveness and efficiency, but they are subject to changes in covariate conditions such as clothing and carrying status. Therefore, we propose a gait energy response function (GERF) that transforms a gait energy (i.e., an intensity value) of a silhouette-based gait feature into a value more suitable for handling these covariate conditions. Additionally, since the discrimination capability of gait energies, as well as the degree to which they are affected by the covariate conditions, differs among body parts, we extend the GERF framework to spatially dependent GERF (SD-GERF) which accounts for spatial dependence. Moreover, the proposed GERFs are represented as a vector in the transformation lookup table and are optimized through an efficient generalized eigenvalue problem in a closed form. Finally, two post-processing techniques, Gabor filtering and spatial metric learning, are employed for the transformed gait features to boost the accuracy. Experimental results with three publicly available datasets including clothing and carrying status variations show the state-of-the-art performance of the proposed method compared with other state-of-the-art methods.

Gait Feature Fusion using Factorial HMM

2010 ◽  
pp. 189-206 ◽  
Author(s):  
Jimin Liang ◽  
Changhong Chen ◽  
Heng Zhao ◽  
Haihong Hu ◽  
Jie Tian
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Sample Size ◽  
Feature Fusion ◽  
Hidden Markov ◽  
Small Sample Size ◽  
Gait Recognition ◽  
Small Sample ◽  
Gait Features ◽  
Gait Feature

Multisource information fusion technology offers a promising solution to the development of a superior classification system. For gait recognition problem, information fusion is necessary to be employed under at least three circumstances: 1) multiple gait feature fusion, 2) multiple view gait sequence fusion, and 3) gait and other biometrics fusion. Feature concatenation is the most popular methodology to integrate multiple features. However, because of the high dimensional gait data size and small available number of training samples, feature concatenation typically leads to the well-known curse of dimensionality and the small sample size problems. In this chapter, we explore the factorial hidden Markov model (FHMM), an extended hidden Markov model (HMM) with a multiple layer structure, as a feature fusion framework for gait recognition. FHMM provides an alternative to combining several gait features without concatenating them into a single augmented feature, thus, to some extent, overcomes the curse of dimensionality and small sample size problem for gait recognition. Three gait features, the frieze feature, wavelet feature, and boundary signature, are adopted in the numerical experiments conducted on CMU MoBo database and CASIA gait database A. Besides the cumulative matching score (CMS) curves, McNemar’s test is employed to check on the statistical significance of the performance difference between the recognition algorithms. Experimental results demonstrate that the proposed FHMM feature fusion scheme outperforms the feature concatenation method.

scholarly journals Deep Large Margin Nearest Neighbor for Gait Recognition

2021 ◽  
Vol 30 (1) ◽  
pp. 604-619
Author(s):  
Wanjiang Xu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Video Surveillance ◽  
Computational Efficiency ◽  
Nearest Neighbor ◽  
Gait Recognition ◽  
Large Margin ◽  
Extensive Evaluation ◽  
Gait Features ◽  
Gait Feature

Abstract Gait recognition in video surveillance is still challenging because the employed gait features are usually affected by many variations. To overcome this difficulty, this paper presents a novel Deep Large Margin Nearest Neighbor (DLMNN) method for gait recognition. The proposed DLMNN trains a convolutional neural network to project gait feature onto a metric subspace, under which intra-class gait samples are pulled together as small as possible while inter-class samples are pushed apart by a large margin. We provide an extensive evaluation in terms of various scenarios, namely, normal, carrying, clothing, and cross-view condition on two widely used gait datasets. Experimental results demonstrate that the proposed DLMNN achieves competitive gait recognition performances and promising computational efficiency.

The Research on Shape Context Based on Gait Sequence Image

2018 ◽  
Vol 9 (2) ◽  
pp. 21-35
Author(s):  
Rong Wang ◽  
Yongkang Liu ◽  
Mengnan Hu
Keyword(s):  
Minimum Distance ◽  
Gait Cycle ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Polar Coordinates ◽  
Target Area ◽  
Shape Context ◽  
Feature Extraction Method ◽  
Sampling Points ◽  
Gait Feature

A gait feature extraction method based on resampling shape context is proposed in this article. First, the moving target detection is carried out to obtain the target area of the human body. Second, the gait cycle is measured, and the contour points and the lower limb joints are selected as sampling points. Then, the different sampling points is placed in the polar coordinates of the origin, the number of sampling points in different cells is counted as the shape context; Finally, the feature vectors are constructed according to the shape context, and the minimum distance is used for classification and recognition. Simulation experiments based on resampling shape context are tested in CASIA gait Database A and Database B. The experimental results show that the method proposed in this article has a lower computational complexity and higher recognition rate when compared with the original shape context method, which can be used for gait recognition.

scholarly journals Investigation of robust gait recognition for different appearances and camera view angles

2021 ◽  
Vol 11 (5) ◽  
pp. 3977
Author(s):  
Chirawat Wattanapanich ◽  
Hong Wei ◽  
Wijittra Petchkit
Keyword(s):  
Recognition Accuracy ◽  
Gait Recognition ◽  
Principal Component ◽  
Personal Identification ◽  
Support Vector ◽  
Small Company ◽  
Feature Descriptors ◽  
Camera View ◽  
Gait Feature ◽  
Appearance Changes

A gait recognition framework is proposed to tackle the challenge of unknown camera view angles as well as appearance changes in gait recognition. In the framework, camera view angles are firstly identified before gait recognition. Two compact images, gait energy image (GEI) and gait modified Gaussian image (GMGI), are used as the base gait feature images. Histogram of oriented gradients (HOG) is applied to the base gait feature images to generate feature descriptors, and then a final feature map after principal component analysis (PCA) operations on the descriptors are used to train support vector machine (SVM) models for individuals. A set of experiments are conducted on CASIA gait database B to investigate how appearance changes and unknown view angles affect the gait recognition accuracy under the proposed framework. The experimental results have shown that the framework is robust in dealing with unknown camera view angles, as well as appearance changes in gait recognition. In the unknown view angle testing, the recognition accuracy matches that of identical view angle testing in gait recognition. The proposed framework is specifically applicable in personal identification by gait in a small company/organization, where unintrusive personal identification is needed.

Color-mapped contour gait image for cross-view gait recognition using deep convolutional neural network

2019 ◽  
Vol 18 (01) ◽  
pp. 1941012
Author(s):  
G. Merlin Linda ◽  
G. Themozhi ◽  
Sudheer Reddy Bandi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Receptive Fields ◽  
Deep Convolutional Neural Network ◽  
Color Mapping ◽  
Difference Of Gaussians ◽  
Gait Feature ◽  
Hysteresis Thresholding

In recent decades, gait recognition has garnered a lot of attention from the researchers in the IT era. Gait recognition signifies verifying or identifying the individuals by their walking style. Gait supports in surveillance system by identifying people when they are at a distance from the camera and can be used in numerous computer vision and surveillance applications. This paper proposes a stupendous Color-mapped Contour Gait Image (CCGI) for varying factors of Cross-View Gait Recognition (CVGR). The first contour in each gait image sequence is extracted using a Combination of Receptive Fields (CORF) contour tracing algorithm which extracts the contour image using Difference of Gaussians (DoG) and hysteresis thresholding. Moreover, hysteresis thresholding detects the weak edges from the total pixel information and provides more well-balanced smooth features compared to an absolute one. Second CCGI encodes the spatial and temporal information via color mapping to attain the regularized contour images with fewer outliers. Based on the front view of a human walking pattern, the appearance of cross-view variations would reduce drastically with respect to a change of view angles. This proposed work evaluates the performance analysis of CVGR using Deep Convolutional Neural Network (CNN) framework. CCGI is considered a gait feature for comparing and evaluating the robustness of our proposed model. Experiments conducted on CASIA-B database show the comparisons of previous methods with the proposed method and achieved 94.65% accuracy with a better recognition rate.

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