Human gait recognition based on feature extraction of support vector machine and pattern network algorithm

This paper explored a new part based gait recognition method to address the gait covariate factors. Firstly, three robust parts such as vertical-half, head, and lower leg are cropped from the Gait Energy Image (GEI). Since, these selected parts are not affected by the major gait covariates than other parts. Then, Radon transform is applied to each selected part. Next, standard deviations are computed for the specified radial lines (i.e. angles) such as 0 0 , 300 , 600 , 900 , 1200 and 1500 , since these radial lines cover the horizontal, vertical and diagonal directions. Lastly, fuse the features of three parts at feature level. Finally, Support Vector Machine (SVM) classifier is used for the classification procedure. The considerable amount of experimental trails are conducted on standard gait datasets and also, the correct classification rates (CCR) have shown that our proposed part based representation is robust in the presence of gait covariates.

Download Full-text

Human Gait Recognition System Based on Support Vector Machine Algorithm and Using Wearable Sensors

Sensors and Materials ◽

10.18494/sam.2019.2288 ◽

2019 ◽

Vol 31 (4) ◽

pp. 1335 ◽

Cited By ~ 1

Author(s):

Fangzheng Wang ◽

Lei Yan ◽

Jiang Xiao

Keyword(s):

Support Vector Machine ◽

Gait Recognition ◽

Wearable Sensors ◽

Recognition System ◽

Human Gait ◽

Support Vector ◽

Support Vector Machine Algorithm

Download Full-text

Analysis of Gait Flow Image and Gait Gaussian Image Using Extension Neural Network for Gait Recognition

Deep Learning and Neural Networks ◽

10.4018/978-1-7998-0414-7.ch025 ◽

2020 ◽

pp. 429-449 ◽

Cited By ~ 1

Author(s):

Parul Arora ◽

Smriti Srivastava ◽

Shivank Singhal

Keyword(s):

Feature Extraction ◽

Nearest Neighbor ◽

Gait Recognition ◽

Human Gait ◽

Support Vector ◽

Gaussian Image ◽

Model Free ◽

Flow Image ◽

Flow Features ◽

A New Technique

This paper proposes a new technique to recognize human gait by combining model free feature extraction approaches and a classifier. Gait flow image (GFI) and gait Gaussian image (GGI) are the two feature extraction techniques used in combination with ENN. GFI is a gait period based technique, uses optical flow features. So it directly focuses on dynamic part of human gait. GGI is another gait period based technique, computed by applying Gaussian membership function on human silhouettes. Next, ENN has been used as a classifier which combines the extension theory and neural networks. All the study has been done on CASIA-A and OU-ISIR treadmill B databases. The results derived using ENN are compared with SVM (support vector machines) and NN (Nearest neighbor) classifiers. ENN proved to give good accuracy and less iteration as compared to other traditional methods.

Download Full-text

Support Vector Machine vs Pattern Network for Gait Recognition

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse/v7i7/01705 ◽

2017 ◽

Vol 7 (7) ◽

pp. 352

Author(s):

Shahla A. AbdAlKader

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Gait Recognition ◽

Experimental Results ◽

Human Gait ◽

Support Vector ◽

Identity Verification ◽

Human Recognition ◽

Biometric Information

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.

Download Full-text

Analysis of Gait Flow Image and Gait Gaussian Image Using Extension Neural Network for Gait Recognition

International Journal of Rough Sets and Data Analysis ◽

10.4018/ijrsda.2016040104 ◽

2016 ◽

Vol 3 (2) ◽

pp. 45-64 ◽

Cited By ~ 8

Author(s):

Parul Arora ◽

Smriti Srivastava ◽

Shivank Singhal

Keyword(s):

Feature Extraction ◽

Nearest Neighbor ◽

Gait Recognition ◽

Human Gait ◽

Support Vector ◽

Gaussian Image ◽

Model Free ◽

Flow Image ◽

Flow Features ◽

A New Technique

This paper proposes a new technique to recognize human gait by combining model free feature extraction approaches and a classifier. Gait flow image (GFI) and gait Gaussian image (GGI) are the two feature extraction techniques used in combination with ENN. GFI is a gait period based technique, uses optical flow features. So it directly focuses on dynamic part of human gait. GGI is another gait period based technique, computed by applying Gaussian membership function on human silhouettes. Next, ENN has been used as a classifier which combines the extension theory and neural networks. All the study has been done on CASIA-A and OU-ISIR treadmill B databases. The results derived using ENN are compared with SVM (support vector machines) and NN (Nearest neighbor) classifiers. ENN proved to give good accuracy and less iteration as compared to other traditional methods.

Download Full-text

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

MALAYSIAN JOURNAL OF COMPUTING ◽

10.24191/mjoc.v5i2.8947 ◽

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.

Download Full-text