Face Recognition with Local Binary Patterns, Spatial Pyramid Histograms and Naive Bayes Nearest Neighbor Classification

2009 ◽  
Author(s):  
Daniel Maturana ◽  
Domingo Mery ◽  
Álvaro Soto
Keyword(s):  
Face Recognition ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Local Binary Patterns ◽  
Naïve Bayes ◽  
Nearest Neighbor Classification ◽  
Neighbor Classification ◽  
Spatial Pyramid

scholarly journals DENSE SIFT FEATURE AND LOCAL NAIVE BAYES NEAREST NEIGHBOR FOR FACE RECOGNITION

2017 ◽  
Vol 1 (28) ◽  
pp. 56-63
Author(s):  
Khanh Ngan Chau ◽  
Nghi Thanh Doan
Keyword(s):  
Face Recognition ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Human Face ◽  
Scale Invariant ◽  
Adaboost Algorithm ◽  
The Face ◽  
Benchmark Datasets ◽  
Scale Invariant Feature

Human face recognition is a technology which is widely used in life. There have been much effort on developing face recognition algorithms. In this paper, we present a new methodology that combines Haar Like Features - Cascade of Boosted Classifiers, Dense Scale-Invariant Feature Transform (DSIFT), Local Naive Bayes Nearest Neighbor (LNBNN) algorithm for the recognition of human face. We use Haar Like Features and the combination  of AdaBoost algorithm and Cascade stratified model to detect and extract the face image, the DSIFT descriptors of the image are computed only for the aligned and cropped face image.Then, we apply the LNBNN algorithms for object recognition. Numerical testing on several benchmark datasets using our proposed method for facerecognition gives the better results than other methods. The accuracies obtained by LNBNN method is 99.74 %.

scholarly journals Exclusive lasso-based k-nearest-neighbor classification

2021 ◽  
Author(s):  
Lin Qiu ◽  
Yanpeng Qu ◽  
Changjing Shang ◽  
Longzhi Yang ◽  
Fei Chao ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Classification ◽  
Neighbor Classification
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