Enhanced feature selections of Adaboost training for face detection using genetic algorithm
A wide variety of face detection techniques have been proposed over the past decades. Generally, a large number of features are required to be selected for training purposes. Often some of these features are irrelevant and do not contribute directly to the face detection techniques. This creates unn...
محفوظ في:
المؤلف الرئيسي: | Mohd. Zin, Zalhan |
---|---|
التنسيق: | أطروحة |
اللغة: | English |
منشور في: |
2007
|
الموضوعات: | |
الوصول للمادة أونلاين: | http://eprints.utm.my/id/eprint/6427/1/ZalhanMohdZinMFKE2007.pdf |
الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
مواد مشابهة
-
Performances comparison of principal component and linear discriminant analysis based face biometrics using artificial neural network and adaboost
بواسطة: Chan , Lih Heng
منشور في: (2010) -
Wavelet and moment invariants based features selection using voronoi diagram for face recognition
بواسطة: Meethongjan, Kittikhun
منشور في: (2013) -
Performance evaluation measures for adaboost algorithms in face detection
بواسطة: Abd Rahman, Mohd Khirulerwan
منشور في: (2010) -
Enhancement of genetic algorithm for diabetic patient diet planning
بواسطة: Heng, Hui Xian
منشور في: (2015) -
Automatic classification of power quality disturbances using optimal feature selection based algorithm
بواسطة: Khokhar, Suhail
منشور في: (2016)