Unsupervised feature selection method for improved human gait recognition
المؤلف | Rida I. |
المؤلف | Al-Maadeed, Somaya |
المؤلف | Bouridane A. |
تاريخ الإتاحة | 2022-05-19T10:23:12Z |
تاريخ النشر | 2015 |
اسم المنشور | 2015 23rd European Signal Processing Conference, EUSIPCO 2015 |
المصدر | Scopus |
المعرّف | http://dx.doi.org/10.1109/EUSIPCO.2015.7362559 |
الملخص | Gait recognition is an emerging biometric technology which aims to identify people purely through the analysis of the way they walk. The technology has attracted interest as a method of identification because it is non-invasiveness since it does not require the subject's cooperation. However, 'covariates' which include clothing, carrying conditions, and other intra-class variations affect the recognition performances. This paper proposes an unsupervised feature selection method which is able to select most relevant discriminative features for human recognition to alleviate the impact of covariates so as to improve the recognition performances. The proposed method has been evaluated using CASIA Gait Database (Dataset B) and the experimental results demonstrate that the proposed technique achieves 85.43 % of correct recognition. |
اللغة | en |
الناشر | Institute of Electrical and Electronics Engineers Inc. |
الموضوع | Biometrics Entropy Feature extraction Gait analysis Signal processing Biometric technology Discriminative features gait Human gait recognition Intra-class variation Method of identifications Model free Unsupervised feature selection Pattern recognition |
النوع | Conference Paper |
الصفحات | 1128-1132 |
الملفات في هذه التسجيلة
الملفات | الحجم | الصيغة | العرض |
---|---|---|---|
لا توجد ملفات لها صلة بهذه التسجيلة. |
هذه التسجيلة تظهر في المجموعات التالية
-
علوم وهندسة الحاسب [2288 items ]