Facial Feature Point Extraction for Object Identification Using Discrete Contourlet Transform and Principal Component Analysis

2013 ◽  
pp. 235-274
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Facial Feature ◽  
Component Analysis ◽  
Object Identification ◽  
Feature Point ◽  
Contourlet Transform ◽  
Feature Point Extraction

Study on Image Fusion Model Based on HIS Transform and Nonsubsampled Contourlet Transform

2012 ◽  
Vol 500 ◽  
pp. 659-665
Author(s):  
Min Cao ◽  
Shan Shan Tan ◽  
Quan Fei Shen
Keyword(s):  
Principal Component Analysis ◽  
Wavelet Transform ◽  
Image Fusion ◽  
Principal Component ◽  
Component Analysis ◽  
Contourlet Transform ◽  
Fusion Method ◽  
Nonsubsampled Contourlet Transform ◽  
Fusion Model ◽  
His Transform

After analysising the principle of nonsubsampled contourlet transform, the image fusion model based on HIS transform and nonsubsampled contourlet transform is proposed. By taking of ALOS image as an example, the image fusion of multi-spectral band and panchromatic band at the same time is carried out by different fusion methods such as the method combining HIS transform and nonsubsampled contourlet transform (NSCT), HIS transform fusion method, principal component analysis (PCA), Brovey and static wavelet transform (SWT). By calculating the quantitative evaluation indicators of the different fused image, it is conclued that the fusion effection of static wavelet transform fusion method and nonsubsampled contourlet transform fusion method is better than the common methods such as HIS transform, principal component analysis and Brovey. In particular, the image fusion effection of nonsubsampled contourlet transform method, which betterly maintains the image spectral information while improving image spatial resolution at the same time, is superior than the fusion evaluation of static wavelet transform fusion method.

scholarly journals A Hybrid Enhanced Real-Time Face Recognition Model using Machine Learning Method with Dimension Reduction

2021 ◽  
Vol 1 (3) ◽  
pp. 12-16
Author(s):  
Jaya Kumari ◽  
◽  
Kailash Patidar ◽  
Mr. Gourav Saxena ◽  
Mr. Rishi Kushwaha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Face Recognition ◽  
Technology Implementation ◽  
Principal Component ◽  
Facial Feature ◽  
Component Analysis ◽  
Experimental Result ◽  
Scale Invariant ◽  
Recognition Model

Face recognition techniques play a crucial role in numerous disciplines of data security, verification, and authentication. The face recognition algorithm selects a face attribute from an image datasets. Recognize identification is an authentication device for verification as well as validation having both data analysis and feasible significance. The facerecognizing centered authentication framework can further be considered an AI technology implementation for instantly identifying a particular image. In this research, we are presenting a hybrid face recognition model (HFRM) using machine learning methods with “Speed Up Robust Features” (SURF), “scale-invariant feature transform” (SIFT), Locality Preserving Projections (LPP) &Principal component analysis (PCA) method. In the proposed HFRM model SURF method mainly detects the local feature efficiently. SIFT method mainly utilizes to detect the local features and recognize them. LPP retains the local framework of facial feature area which is generally quite meaningful than on the sequence kept by a 'principal component analysis (PCA) as well as “linear discriminate analysis” (LDA). The proposed HFRM method is compared with the existing (H. Zaaraoui et al., 2020) method and the experimental result clearly shows the outstanding performance in terms of detection rate and accuracy % over existing methods.

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