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 Gait Recognition of Acceleration Sensor for Smart Phone Based on Multiple Classifier Fusion

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Zhan Huan ◽  
Xuejie Chen ◽  
Shiyun Lv ◽  
Hongyang Geng
Keyword(s):  
Gait Cycle ◽  
Linear Time ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Classifier Fusion ◽  
Velocity Change ◽  
Multiple Classifiers ◽  
Acceleration Sensors ◽  
Gait Features ◽  
New Feature

Gait, as a kind of biological feature, has a profound value in personnel identification. This paper analyzes gait characteristics based on acceleration sensors of smart phones and proposes a new gait recognition method. First, in view of the existing methods in the process of extraction of gait features, a large number of redundant calculations, cycle detection error, and the phase deviation issue during the week put forward the Shape Context (SC) and Linear Time Normalized (LTN) combining SCLTN calibration method of gait cycle sequence matching, to represent the whole extract typical gait cycle gait. In view of the existing extracted gait features are still some conventional features; the velocity change of relatively uniform acceleration and the change of acceleration per unit time are proposed as new features. Secondly, combining new features with traditional features to form a new feature is set for training alternative feature set, from which the training time and recognition effect of multiple classifiers are screened. Finally, a new multiclassifier fusion method, Multiple Scale Voting (MSV), is proposed to fuse the results of Multiple classifiers to obtain the final classification results. In order to verify the performance of the proposed method, gait data of 32 testers are collected. The final experimental results show that the new feature has good separability, and the recognition rate of fusion feature set after MSV algorithm is 98.42%.

Recognition of Colored Face, Based on an Improved Color Local Binary Pattern

2019 ◽  
Vol 33 (04) ◽  
pp. 1956004 ◽  
Author(s):  
Zhi-Ming Li ◽  
Zheng-Hai Huang ◽  
Wen-Juan Li
Keyword(s):  
Face Recognition ◽  
Sampling Method ◽  
Local Binary Pattern ◽  
Hamming Distance ◽  
Recognition Rate ◽  
Computational Cost ◽  
Feature Extraction Method ◽  
Sampling Points ◽  
The Given ◽  
Color Face Recognition

In this paper, a novel feature extraction method based on an improved color local binary pattern (LBP) is proposed for color face recognition. Firstly, in a given neighborhood of every pixel, we choose some sampling points from three color channels simultaneously and the numbers of the sampling points from every channel may be different. Secondly, we use a new rule to select the threshold which does not always locate in the geometrical center of the given neighborhood. Thirdly, in order to excavate the potential of the proposed sampling method, we use the [Formula: see text]-uniform LBP to obtain the binary code of each pixel. In addition, we embed the Hamming distance into our method for improving the recognition rate of the proposed method. For evaluating the performance of our method, we implement the proposed method and several related methods on five public face databases: FERET, CMU-PIE, Georgia, FEI and Asian databases. Experimental results show that our method possesses higher recognition rates and lower computational cost than other related color face recognition methods.

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.

View-Invariant Gait Recognition Based on Head and Shoulder Shape Analysis

2014 ◽  
Vol 568-570 ◽  
pp. 705-709
Author(s):  
Guo Zhen Wang ◽  
Hua Zhang ◽  
Li Jia Wang ◽  
Zhen Jie Wang
Keyword(s):  
Gait Analysis ◽  
Shape Analysis ◽  
Gait Cycle ◽  
Average Distance ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Invariant Method ◽  
Discriminative Feature ◽  
Mean Shape ◽  
The Mean

To improve the recognition rate and resolve the view problem in gait analysis, a view-invariant method is presented. This method extracts the mean shape of the head and shoulder models in a gait cycle by using the Procrustes Shape Analysis (PSA) algorithm. Then, the mean shape is converted from 2-D space to 1-D space by unfolding the 2D mean shape from the top point of the head. Furthermore, the piecewise approximation (PA) of the unfolded mean shape is segmented corresponding to the average distance value. The obtained PA is adopted for estimating views and recognizing the gait. Finally, the presented method is conducted on the CASIA B database. The results illustrate that the PA is a powerful discriminative feature for view estimation and gait recognition, and the performance has been improved when there is view variation.

scholarly journals Multimodal Low Resolution Face and Frontal Gait Recognition from Surveillance Video

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1013
Author(s):  
Sayan Maity ◽  
Mohamed Abdel-Mottaleb ◽  
Shihab S. Asfour
Keyword(s):  
Face Recognition ◽  
High Resolution ◽  
Gait Cycle ◽  
Gait Recognition ◽  
Recognition System ◽  
Multimodal Fusion ◽  
Surveillance Video ◽  
Low Resolution ◽  
Model Free ◽  
Face Images

Biometric identification using surveillance video has attracted the attention of many researchers as it can be applicable not only for robust identification but also personalized activity monitoring. In this paper, we present a novel multimodal recognition system that extracts frontal gait and low-resolution face images from frontal walking surveillance video clips to perform efficient biometric recognition. The proposed study addresses two important issues in surveillance video that did not receive appropriate attention in the past. First, it consolidates the model-free and model-based gait feature extraction approaches to perform robust gait recognition only using the frontal view. Second, it uses a low-resolution face recognition approach which can be trained and tested using low-resolution face information. This eliminates the need for obtaining high-resolution face images to create the gallery, which is required in the majority of low-resolution face recognition techniques. Moreover, the classification accuracy on high-resolution face images is considerably higher. Previous studies on frontal gait recognition incorporate assumptions to approximate the average gait cycle. However, we quantify the gait cycle precisely for each subject using only the frontal gait information. The approaches available in the literature use the high resolution images obtained in a controlled environment to train the recognition system. However, in our proposed system we train the recognition algorithm using the low-resolution face images captured in the unconstrained environment. The proposed system has two components, one is responsible for performing frontal gait recognition and one is responsible for low-resolution face recognition. Later, score level fusion is performed to fuse the results of the frontal gait recognition and the low-resolution face recognition. Experiments conducted on the Face and Ocular Challenge Series (FOCS) dataset resulted in a 93.5% Rank-1 for frontal gait recognition and 82.92% Rank-1 for low-resolution face recognition, respectively. The score level multimodal fusion resulted in 95.9% Rank-1 recognition, which demonstrates the superiority and robustness of the proposed approach.

Design of Gait Recognition System Based on FPGA and DSP

2014 ◽  
Vol 687-691 ◽  
pp. 3861-3868
Author(s):  
Zheng Hong Deng ◽  
Li Tao Jiao ◽  
Li Yan Liu ◽  
Shan Shan Zhao
Keyword(s):  
Monitoring System ◽  
Nearest Neighbor ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Digital Signal ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Zernike Moment ◽  
Intelligent Monitoring ◽  
The One

According to the trend of the intelligent monitoring system, on the basis of the study of gait recognition algorithm, the intelligent monitoring system is designed based on FPGA and DSP; On the one hand, FPGA’s flexibility and fast parallel processing algorithms when designing can be both used to avoid that circuit can not be modified after designed; On the other hand, the advantage of processing the digital signal of DSP is fully taken. In the feature extraction and recognition, Zernike moment is selected, at the same time the system uses the nearest neighbor classification method which is more mature and has good real-time performance. Experiments show that the system has high recognition rate.

scholarly journals Comparison of shortest sailing distance through random and regular sampling points

2004 ◽  
Vol 61 (1) ◽  
pp. 140-147 ◽  
Author(s):  
Alf Harbitz ◽  
Michael Pennington
Keyword(s):  
Random Sampling ◽  
Minimum Distance ◽  
Barents Sea ◽  
Pandalus Borealis ◽  
The Barents Sea ◽  
Theoretical Sampling ◽  
Sampling Points ◽  
Regular Sampling ◽  
The Mean ◽  
The Difference

Abstract The shortest sailing distance through n sampling points is calculated for simple theoretical sampling domains (square and circle) as well as for a rather irregular and concavely shaped real sampling domain in the Barents Sea. The sampling sites are either located at the nodes of a square grid (regular sampling) or they are randomly distributed. For n less than ten, the exact shortest sailing distance is derived. For larger n, a traveling salesman algorithm (simulated annealing) was applied, and its bias (distance from true minimum) was estimated based on a case where the true minimum distance was known. In general, the average minimum sailing distance based on random sampling was considerably shorter than for regular sampling, and the difference increased with sample size until an asymptotic value was reached at about n=60 for a square domain. For the sampling domain in the Barents Sea used for shrimp (Pandalus borealis) abundance surveys (n=118 stations), the cruise-track lengths based on random sampling were approximately normally distributed. The mean sailing distance was 18% shorter than the cruise track for regular sampling and the standard deviation equalled 2.6%.

scholarly journals Texture Feature Extraction Method for Ground Nephogram Based on Hilbert Spectrum of Bidimensional Empirical Mode Decomposition

2014 ◽  
Vol 31 (9) ◽  
pp. 1982-1994 ◽  
Author(s):  
Xiaoying Chen ◽  
Aiguo Song ◽  
Jianqing Li ◽  
Yimin Zhu ◽  
Xuejin Sun ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Recognition Rate ◽  
Texture Feature ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Feature Extraction Method ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Bidimensional Empirical Mode Decomposition

Abstract It is important to recognize the type of cloud for automatic observation by ground nephoscope. Although cloud shapes are protean, cloud textures are relatively stable and contain rich information. In this paper, a novel method is presented to extract the nephogram feature from the Hilbert spectrum of cloud images using bidimensional empirical mode decomposition (BEMD). Cloud images are first decomposed into several intrinsic mode functions (IMFs) of textural features through BEMD. The IMFs are converted from two- to one-dimensional format, and then the Hilbert–Huang transform is performed to obtain the Hilbert spectrum and the Hilbert marginal spectrum. It is shown that the Hilbert spectrum and the Hilbert marginal spectrum of different types of cloud textural images can be divided into three different frequency bands. A recognition rate of 87.5%–96.97% is achieved through random cloud image testing using this algorithm, indicating the efficiency of the proposed method for cloud nephogram.

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.

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