Biometric Gait Recognition Based on Machine Learning Algorithms

Sayed, Mohamed

doi:10.3844/jcssp.2018.1064.1073

Cited by 12 publications

(6 citation statements)

References 26 publications

(28 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The difference between the two is that the external clustering effect evaluation method has the correct sample clustering result label as a reference; that is, the clustering label of the sample point is given in whole or in part. Thus, the actual clustering results can be compared with the given clustering labels, and a reliable clustering effect evaluation can be obtained [ 16 ]. However, for the samples to be clustered, the correct clustering labels are often difficult to obtain.…”

Section: Current Status and Methods Of Gait Recognition From Plantar ...mentioning

confidence: 99%

See 1 more Smart Citation

Gait Recognition Based on the Plantar Pressure Data of Ice and Snow Athletes

Li²,

Zhao

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The study of plantar pressure has become a research consensus in the field of biomechanics. The purpose of this paper is to study some lower limb movements in the daily activities of ice and snow athletes to obtain relevant data so as to carry out gait recognition analysis research. This paper selects the average foot pressure, forefoot foot pressure, front and rear foot pressure, foot pressure, toe pressure, 2–5 toe pressure, standing with eyes closed, x- and y-axes speed, foot length, foot width, and other actions of ice and snow athletes. Therefore, correlation analysis, work analysis, and curve fitting analysis were carried out on the joint motion in a single gait cycle. The collection and application of foot pressure and foot posture information are also analyzed. According to the plantar structure, the sole is divided into four parts. The maximum pressure point and coordinates of each part, the pressure center point, the ratio of the width and height of the sole of the foot, and so on are extracted as the haptic features of the gait. The experimental data shows that it can be seen that if the plantar area is divided in advance and the weight of each area is marked, whether standing, walking, or standing with one leg closed eyes can achieve better recognition results, and the accuracy rate is all more than 90 percent. The average recognition accuracy rate using the method of dividing four regions is only about 80%, and the accuracy rate of recognition using the method of dividing eight regions is 82%. It can be seen that the features extracted by the FCM model proposed in this paper contain more information of the plantar pressure image, and the accuracy rate is higher in the classification and recognition.

show abstract

Section: Current Status and Methods Of Gait Recognition From Plantar ...mentioning

confidence: 99%

“…us, the actual clustering results can be compared with the given clustering labels, and a reliable clustering effect evaluation can be obtained [16]. However, for the samples to be clustered, the correct clustering labels are often difficult to obtain.…”

Section: Evaluation Of Clustering Effectmentioning

confidence: 99%

Gait Recognition Based on the Plantar Pressure Data of Ice and Snow Athletes

Li²,

Zhao

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Feature extraction is the process of extracting features from each silhouette image to get the CoM of each silhouette image. The CoM (centroid) was generally obtained by using the average coordinate ( ) value of each pixel composes the object [10]. The center of mass value was stored in the matrix in the form of .mat.…”

Section: Feature Extractionmentioning

confidence: 99%

“…7and Eq. 8shows the calculation the average CoM on the axis of coordinates ) , ( y x based on the number of pixels [10].…”

Section: mentioning

confidence: 99%

Human Gait Feature Extraction based-on Silhouette and Center of Mass

Jannah¹,

Madenda²,

Maulana³

et al. 2019

IJACSA

View full text Add to dashboard Cite

When someone walks, there is a repetitive movement or coordinated cycle that forms a gait. Gait is different, unique and difficult to imitate. This characteristic makes gait one of the biometrics to find out one's identity. Gait analysis is needed in the development of biometric technology, such as in the field of security surveillance and the health sector to monitor gait abnormalities. The center of mass is the unique point of every object that has a role in the study of humans walking. Each person has a different center of mass. In this research, through a series of processes in image processing such as video acquisition, segmentation, silhouette formation, and feature extraction, the center of mass of the human body can be identified using a webcam with the resolution of 640 x 480 pixels and the frame rate of 30 frames/second. The results obtained from this research were gait frames of 510 frames from 17 pedestrian videos. Segmentation process using background subtraction separates the pedestrian object image from the background. Silhouette gait was produced from a series of image enhancement processes to eliminate noise that interferes the image quality. Based on the silhouette, feature extraction provides the center of mass to distinguish each individual's gait. The sequence of center of mass can be further processed for characterizing human gait cycle for various purposes.

show abstract

“…A feature reduction is necessary to extract beneficial and informative features for classification purposes. Furthermore, the dimensionality of features extracted from input thermal images usually plays an important role in conventional classification accuracy and deserves more attention from the literature (Sayed, 2018a), (Sayed, 2018b). It is worth mentioning that each feature is masking a few pixels of the input image with a twodimensional array of values and matches common characteristics of the images.…”

Section: Conventional Neural Network Modelsmentioning

confidence: 99%

Thermal Face Authentication with Convolutional Neural Network

Sayed¹,

Baker²

2018

Journal of Computer Science

Self Cite

View full text Add to dashboard Cite

Matching thermal face images as a method of biometric authentication has gained increasing interest because of its advantage of tracking a target object at night and in total darkness. Therefore, for security purposes, it has become highly favourable and has extensive applications, for instance, in video surveillance at night. The aim of this study is to present a simple and efficient deep learning model, which accurately predicts person identification. A pre-trained Convolutional Neural Network (CNN) is employed to extract the features of the multiple convolution layers of the low resolutions' thermal infrared images. To run the program and evaluate the performance, we use a sample of 1500 resized thermal images, each with resolution 181×161 pixels. The sample comprises of images that were captured within different time-lapse and with diverse emotions, poses and lighting conditions. The proposed approach is effective compared to the state-of-the-art thermal face recognition algorithms and achieves impressive accuracy of 99.6% with less processing and training times.

show abstract

Biometric Gait Recognition Based on Machine Learning Algorithms

Cited by 12 publications

References 26 publications

Gait Recognition Based on the Plantar Pressure Data of Ice and Snow Athletes

Gait Recognition Based on the Plantar Pressure Data of Ice and Snow Athletes

Human Gait Feature Extraction based-on Silhouette and Center of Mass

Thermal Face Authentication with Convolutional Neural Network

Contact Info

Product

Resources

About