期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2019
卷号：97
期号：4
页码：1343-1354
出版社：Journal of Theoretical and Applied
摘要：The field of Human Gait Recognition is one of the active research fields. The number of studies has increased which indicates that this area is important. The present study has many benefits as it enables us to recognize a person through his gait in front of a camera even if the gait picture is taken from a long distance with a low-quality camera. Moreover, many other biometrics require special equipment; and some of them require taking the picture from a short distance. This study aims at finding a solution with high quality and efficiency in the process of recognizing human gait. This is achieved by providing a strong training set. For this purpose, we created two training sets. The first depends on figuring out human features by using Gait Energy Image (GEI). The second set depends upon figuring out human features by using Gait Energy Image (GEI), and then filtering it by using Gabor Filter. This study was conducted as an experimental research to gain ideal parameter values for Gabor Filter that contributes to enhancing the training sets samples that leads to increasing the system performance efficiency, and we found the best values for Gabor filter parameters for our system is kernel =3 ,theta = 225 , 𝛾=1.25, 𝜓=0, 𝜆=3.5, and a bandwidth =2.8. The training and testing were conducted by using (SVM) Algorithm, the study uses the gait database (CASIA-B) to evaluate our system, Then, we used (CCR) for Performance measurement . The study reached good ratios for all views with an average of 76.50% for (GEI+GABOR FILTER) and 76.42 for (GEI). Moreover, the study achieved an ideal percentage of 78.10% for (GEI+GABOR FILTER) and 77.66 for (GEI), as compared to previous studies of the positions of normal-walking, wearing-coat, and carrying-bags at a view of 90.
关键词：biometrics ، Human Gait Recognition ،(GEI) Gait Energy Image ،Gabor filter ، Support Vector Machine; the correct classification، rate (CCR).
