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.

scholarly journals Research on Recognition Method of Driving Fatigue State Based on Sample Entropy and Kernel Principal Component Analysis

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 701 ◽  
Author(s):  
Beige Ye ◽  
Taorong Qiu ◽  
Xiaoming Bai ◽  
Ping Liu
Keyword(s):  
Principal Component Analysis ◽  
Recognition Accuracy ◽  
Principal Component ◽  
Component Analysis ◽  
Sample Entropy ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Recognition Method ◽  
State Recognition ◽  
Driving Fatigue

In view of the nonlinear characteristics of electroencephalography (EEG) signals collected in the driving fatigue state recognition research and the issue that the recognition accuracy of the driving fatigue state recognition method based on EEG is still unsatisfactory, this paper proposes a driving fatigue recognition method based on sample entropy (SE) and kernel principal component analysis (KPCA), which combines the advantage of the high recognition accuracy of sample entropy and the advantages of KPCA in dimensionality reduction for nonlinear principal components and the strong non-linear processing capability. By using support vector machine (SVM) classifier, the proposed method (called SE_KPCA) is tested on the EEG data, and compared with those based on fuzzy entropy (FE), combination entropy (CE), three kinds of entropies including SE, FE and CE that merged with KPCA. Experiment results show that the method is effective.

scholarly journals Parameters Optimization and Application of SVM Based on PCA-Particle Swarm Algorithm

2019 ◽  
pp. 325-330 ◽  
Author(s):  
Qingmi Yang
Keyword(s):  
Parameter Optimization ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Principal Component ◽  
Pso Algorithm ◽  
Parameters Optimization ◽  
Support Vector ◽  
Particle Swarm Algorithm

The parameter optimization of Support Vector Machine (SVM) has been a hot research direction. To improve the optimization rate and classification performance of SVM, the Principal Component Analysis (PCA) - Particle Swarm Optimization (PSO) algorithm was used to optimize the penalty parameters and kernel parameters of SVM. PSO which is to find the optimal solution through continuous iteration combined with PCA that eliminates linear redundancy between data, effectively enhance the generalization ability of the model, reduce the optimization time of parameters, and improve the recognition accuracy. The simulation comparison experiments on 6 UCI datasets illustrate that the excellent performance of the PCA-PSO-SVM model. The results show that the proposed algorithm has higher recognition accuracy and better recognition rate than simple PSO algorithm in the parameter optimization of SVM. It is an effective parameter optimization method.

scholarly journals An Online Semisupervised Learning Model for Pedestrians’ Crossing Intention Recognition of Connected Autonomous Vehicle Based on Mobile Edge Computing Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Shicai Ji ◽  
Ying Peng ◽  
Hongjia Zhang ◽  
Shengbo Wu
Keyword(s):  
Autonomous Vehicles ◽  
Recognition Accuracy ◽  
Autonomous Vehicle ◽  
Principal Component ◽  
Semisupervised Learning ◽  
Support Vector ◽  
Human Machine Interaction ◽  
Intention Recognition ◽  
Comparison Results ◽  
Pedestrian Crossing

One of the major challenges that connected autonomous vehicles (CAVs) are facing today is driving in urban environments. To achieve this goal, CAVs need to have the ability to understand the crossing intention of pedestrians. However, for autonomous vehicles, it is quite challenging to understand pedestrians’ crossing intentions. Because the pedestrian is a very complex individual, their intention to cross the street is affected by the weather, the surrounding traffic environment, and even his own emotions. If the established street crossing intention recognition model cannot be updated in real time according to the diversity of samples, the efficiency of human-machine interaction and the interaction safety will be greatly affected. Based on the above problems, this paper established a pedestrian crossing intention model based on the online semisupervised support vector machine algorithm (OS3VM). In order to verify the effectiveness of the model, this paper collects a large amount of pedestrian crossing data and vehicle movement data based on laser scanner, and determines the main feature components of the model input through feature extraction and principal component analysis (PCA). The comparison results of recognition accuracy of SVM, S3VM, and OS3VM indicate that the proposed OS3VM model exhibits a better ability to recognize pedestrian crossing intentions than the SVM and S3VM models, and the accuracy achieves 94.83%. Therefore, the OS3VM model can reduce the number of labeled samples for training the classifier and improve the recognition accuracy.

scholarly journals Exploiting Wavelet Transform, Principal Component Analysis, Support Vector Machine, and K-Nearest Neighbors for Partial Face Recognition

2019 ◽  
Vol 3 (2) ◽  
pp. 80-84 ◽  
Author(s):  
Mustafa H. Mohammed Alhabib ◽  
Mustafa Zuhaer Nayef Al-Dabagh ◽  
Firas H. AL-Mukhtar ◽  
Hussein Ibrahim Hussein
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Wavelet Transform ◽  
Principal Component ◽  
Component Analysis ◽  
Input Image ◽  
Personal Identification ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
The Face

Facial analysis has evolved to be a process of considerable importance due to its consequence on the safety and security, either individually or generally on the society level, especially in personal identification. The paper in hand applies facial identification on a facial image dataset by examining partial facial images before allocating a set of distinctive characteristics to them. Extracting the desired features from the input image is achieved by means of wavelet transform. Principal component analysis is used for feature selection, which specifies several aspects in the input image; these features are fed to two stages of classification using a support vector machine and K-nearest neighborhood to classify the face. The images used to test the strength of the suggested method are taken from the well-known (Yale) database. Test results showed the eligibility of the system when it comes to identify images and assign the correct face and name.

A Subblock Matrix Approach to Data-Reduction in Gait Recognition

2011 ◽  
Vol 181-182 ◽  
pp. 902-907
Author(s):  
Xian Ye Ben ◽  
Shi An ◽  
Jian Wang ◽  
Hai Yang Liu
Keyword(s):  
Principal Component Analysis ◽  
Data Reduction ◽  
Nearest Neighbor ◽  
Recognition Accuracy ◽  
Gait Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Redundant Data ◽  
Novel Method

We propose a novel method for data reduction in gait recognition, called Subblock Complete Two Dimensional Principal Component Analysis (SbC2DPCA). GEIs were divided into smaller sub-images and redundant subblocks were adaptively removed. Complete Two Dimensional Principal Component Analysis (C2DPCA) was then applied to every sub-image directly, to acquire a set of projection sub-vectors for both row and column directions and these were synthesized into whole features for subsequent classification using nearest neighbor classifier. We evaluate the proposed gait recognition method on the CASIA gait database. The experimental results and analysis show the recognition accuracy of SbC2DPCA to be superior to C2DPCA, with C2DPCA being a special case of SbC2DPCA. The novelty of the proposed method lies in the adaptive removal of redundant data while extracting local features. This translates to data reduction with very minimal loss of information, as demonstrated by the remarkable recognition accuracy when subjects change clothing or have a backpack.

scholarly journals Concentration-Emission Matrix (CEM) Spectroscopy Combined with GA-SVM: An Analytical Method to Recognize Oil Species in Marine

Molecules ◽  
2020 ◽  
Vol 25 (21) ◽  
pp. 5124
Author(s):  
Yunan Chen ◽  
Ruifang Yang ◽  
Nanjing Zhao ◽  
Wei Zhu ◽  
Xiaowei Chen ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Recognition Accuracy ◽  
Spectral Feature ◽  
Principal Component ◽  
Support Vector ◽  
Classification Methods ◽  
Effective Management ◽  
Algorithm Optimization ◽  
Probabilistic Neural Networks ◽  
Oil Source

The establishment and development of a set of methods of oil accurate recognition in a different environment are of great significance to the effective management of oil spill pollution. In this work, the concentration-emission matrix (CEM) is formed by introducing the concentration dimension. The principal component analysis (PCA) is applied to extract the spectral feature. The classification methods, such as Probabilistic Neural Networks (PNNs) and Genic Algorithm optimization Support Vector Machine (SVM) parameters (GA-SVM), are used for oil identification and the recognition accuracies of the two classification methods are compared. The results show that the GA-SVM combined with PCA has the highest recognition accuracy for different oils. The proposed approach has great potential in rapid and accurate oil source identification.

scholarly journals Research on an SVM Coupling Algorithm of Image Recognition

2016 ◽  
Author(s):  
Ai-Min Yang ◽  
Yang Han ◽  
Jin-Ze Li ◽  
Yu-Hang Pan ◽  
Lei-Jie Shen ◽  
...  
Keyword(s):  
Face Recognition ◽  
Recognition Accuracy ◽  
Algorithm Design ◽  
Principal Component ◽  
Facial Feature ◽  
Face Image ◽  
Support Vector ◽  
Svm Classifier ◽  
Face Image Processing ◽  
Coupling Algorithm

The key links of face recognition are digital image preprocessing, facial feature extraction and pattern recognition, this article aimed at the current problem of slow speed and low recognition accuracy of face recognition , from the above three key links, on the basic of analyzing the therories of Fractional Differential Masks Operator (FDMO), Principal Component Analysis (PCA) and Support Vector Machine (SVM), design a kind of FDMO+PVA+SVM coupling algorithm that applies to face recognition to improve the speed and accuracy of it. To realize FDMO+PCA+SVM coupling algorithm, first, we should apply FDMO to face image processing binary marginalization, the purpose is getting face contour; Then, we apply PCA to get the feature of face image which is disposed by binary marainalization. At last, we can apply One-Against All of the SVM classifier and LibSVM software package to realize face recognition. Beside, the article with nine different coupling algorithm design four groups of experimental reaults on the ORL face database verified by comparative analysic FDMO+PCA+SVM coupling algorithm in the superiority of face recognition accuracy and speed.

Decision Fusion Gait Recognition Based on Bayesian Rule and Support Vector Machine

2013 ◽  
Vol 411-414 ◽  
pp. 1287-1290 ◽  
Author(s):  
Hong Zheng ◽  
Kai Zhang
Keyword(s):  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Recognition Performance ◽  
National Laboratory ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Principal Component ◽  
Decision Fusion ◽  
Support Vector ◽  
Correct Recognition Rate

To distinguish people’s identities, the information is normally included in one gait periodic sequence image. First, the gait energy image for feature extraction of wavelet moments was constructed. After boundary unwrapping, the gait silhouette boundary was extracted and principal component analysis (PCA) was use to obtain its compressed contour features. Then nearest neighbor classifier and support vector machines were applied for classification of these two features. Finally, support vector machine (SVM) on Bayesian rule were used to complete gait recognition with information fusion of different features. The method is evaluated on the National Laboratory of Pattern Recognition (NLPR) gait database and the correct recognition rate is relatively high. The experimental results show that the proposed method has good recognition performance.

OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

2020 ◽  
Vol 5 (2) ◽  
pp. 504
Author(s):  
Matthias Omotayo Oladele ◽  
Temilola Morufat Adepoju ◽  
Olaide ` Abiodun Olatoke ◽  
Oluwaseun Adewale Ojo
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Word Recognition ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Support Vector ◽  
Geometric Features ◽  
Total Length ◽  
Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches

2020 ◽  
Vol 16 (8) ◽  
pp. 1088-1105
Author(s):  
Nafiseh Vahedi ◽  
Majid Mohammadhosseini ◽  
Mehdi Nekoei
Keyword(s):  
Present Report ◽  
Principal Component ◽  
Parp Inhibitors ◽  
Support Vector ◽  
Ann Model ◽  
Statistical Parameters ◽  
Qsar Study ◽  
Data Set ◽  
Test Set ◽  
Non Linear

Background: The poly(ADP-ribose) polymerases (PARP) is a nuclear enzyme superfamily present in eukaryotes. Methods: In the present report, some efficient linear and non-linear methods including multiple linear regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) were successfully used to develop and establish quantitative structure-activity relationship (QSAR) models capable of predicting pEC50 values of tetrahydropyridopyridazinone derivatives as effective PARP inhibitors. Principal component analysis (PCA) was used to a rational division of the whole data set and selection of the training and test sets. A genetic algorithm (GA) variable selection method was employed to select the optimal subset of descriptors that have the most significant contributions to the overall inhibitory activity from the large pool of calculated descriptors. Results: The accuracy and predictability of the proposed models were further confirmed using crossvalidation, validation through an external test set and Y-randomization (chance correlations) approaches. Moreover, an exhaustive statistical comparison was performed on the outputs of the proposed models. The results revealed that non-linear modeling approaches, including SVM and ANN could provide much more prediction capabilities. Conclusion: Among the constructed models and in terms of root mean square error of predictions (RMSEP), cross-validation coefficients (Q2 LOO and Q2 LGO), as well as R2 and F-statistical value for the training set, the predictive power of the GA-SVM approach was better. However, compared with MLR and SVM, the statistical parameters for the test set were more proper using the GA-ANN model.

Sign in / Sign up

Export Citation Format

Share Document