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Gabor and maximum response filters with random forest classifier for face recognition in the wild
المؤلفون المشاركون
See, Yuen Chark
Liew, Eugene
Nur, Norliza Muhammad
المصدر
The International Arab Journal of Information Technology
العدد
المجلد 18، العدد 6 (30 نوفمبر/تشرين الثاني 2021)، ص ص. 797-806، 10ص.
الناشر
جامعة الزرقاء عمادة البحث العلمي
تاريخ النشر
2021-11-30
دولة النشر
الأردن
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
Research on face recognition has been evolving for decades.
There are numerous approaches developed with highly desirable outcomes in constrained environments.
In contrast, approaches to face recognition in an unconstrained environment where varied facial posing, occlusion, aging, and image quality still pose vast challenges.
Thus, face recognition in the unconstrained environment still an unresolved problem.
Many current techniques are not performed well when experimented in unconstrained databases.
Additionally, most of the real-world application needs a good face recognition performance in the unconstrained environment.
This paper presents a comprehensive process aimed to enhance the performance of face recognition in an unconstrained environment.
This paper presents a face recognition system in an unconstrained environment.
The fusion between Gabor filters and Maximum Response (MR) filters with Random Forest classifier is implemented in the proposed system.
Gabor filters are a hybrid of Gabor magnitude filters and Oriented Gabor Phase Congruency (OGPC) filters.
Gabor magnitude filters produce the magnitude response while the OGPC filters produce the phase response of Gabor filters.
The MR filters contain the edge- and bar-anisotropic filter responses and isotropic filter responses.
In the face features selection process, Monte Carlo Uninformative Variable Elimination Partial Least Squares Regression (MC-UVE-PLSR) is used to select the optimal face features in order to minimize the computational costs without compromising the accuracy of face recognition.
Random Forests is used in the classification of the generated feature vectors.
The algorithm performance is evaluated using two unconstrained facial image databases: Labelled Faces in the Wild (LFW) and Unconstrained Facial Images (UFI).
The proposed technique used produces encouraging results in these evaluated databases in which it recorded face recognition rates that are comparable with other state-of-the-art algorithms.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
See, Yuen Chark& Liew, Eugene& Nur, Norliza Muhammad. 2021. Gabor and maximum response filters with random forest classifier for face recognition in the wild. The International Arab Journal of Information Technology،Vol. 18, no. 6, pp.797-806.
https://search.emarefa.net/detail/BIM-1430943
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
See, Yuen Chark…[et al.]. Gabor and maximum response filters with random forest classifier for face recognition in the wild. The International Arab Journal of Information Technology Vol. 18, no. 6 (Nov. 2021), pp.797-806.
https://search.emarefa.net/detail/BIM-1430943
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
See, Yuen Chark& Liew, Eugene& Nur, Norliza Muhammad. Gabor and maximum response filters with random forest classifier for face recognition in the wild. The International Arab Journal of Information Technology. 2021. Vol. 18, no. 6, pp.797-806.
https://search.emarefa.net/detail/BIM-1430943
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references : p. 804-806
رقم السجل
BIM-1430943
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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