Face Feature Selection and Face Recognition using GroupMutual-Boost
نویسندگان
چکیده
منابع مشابه
Illumination Invariant Feature Selection for Face Recognition
We propose a novel hybrid illumination invariant feature selection scheme for face recognition, which is a combination of geometrical feature extraction and linear subspace projection. By local geometry feature enhancement technique, neighborhood histogram equalization (NHE) in our experiment, some illegible edges due to week illumination will be enhanced effectively. Then we applied classic li...
متن کاملGabor Feature Selection for Face Recognition
A discriminative and robust feature kernel enhanced informative Gabor feature is proposed in this paper for face recognition. Mutual information is applied to select a set of informative and non-redundant Gabor features, which are then further enhanced by Kernel methods for recognition. Compared with one of the top performing methods in the 2004 Face Verification Competition (FVC2004), our meth...
متن کاملClass-Dependent Feature Selection for Face Recognition
Feature extraction and feature selection are very important steps for face recognition. In this paper, we propose to use a classdependent feature selection method to select different feature subsets for different classes after using principal component analysis to extract important information from face images. We then use the support vector machine (SVM) for classification. The experimental re...
متن کاملFeature Selection for Pose Invariant Face Recognition
One of the major difficulties in face recognition systems is the in-depth pose variation problem. Most face recognition approaches assume that the pose of the face is known. In this work, we have designed a feature based pose estimation and face recognition system using 2D Gabor wavelets as local feature information. The difference of our system from the existing ones lies in its simplicity and...
متن کاملFeature Selection Using Adaboost for Face Expression Recognition
We propose a classification technique for face expression recognition using AdaBoost that learns by selecting the relevant global and local appearance features with the most discriminating information. Selectivity reduces the dimensionality of the feature space that in turn results in significant speed up during online classification. We compare our method with another leading margin-based clas...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of the Korea Society for Simulation
سال: 2011
ISSN: 1225-5904
DOI: 10.9709/jkss.2011.20.4.013