Face Recognition System Based on Gabor Wavelets Transform, Principal Component Analysis and Support Vector Machine

2020 ◽  
Vol 10 (3) ◽  
pp. 959
Author(s):  
Salar J Rashid ◽  
Abdulqadir I Abdullah ◽  
Mustafa A Shihab
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Support Vector ◽  
Gabor Wavelets ◽  
Face Recognition System ◽  
Wavelets Transform

scholarly journals A Face Recognition System Based on Principal Component Analysis-Wavelet and Support Vector Machines

2019 ◽  
Vol 3 (2) ◽  
pp. 14-20
Author(s):  
Laith R. Fleah ◽  
Shaimaa A. Al-Aubi
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Face Image ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Face Recognition System

Face recognition can represent a key requirement in various types of applications such as human-computer interface, monitoring systems, as well as personal identification. In this paper, design and implement of face recognition system are introduced. In this system, a combination of principal component analysis (PCA) and wavelet feature extraction algorithms with support vector machine (SVM) and K-nearest neighborhood classifier is used. PCA and wavelet transform methods are used to extract features from face image using and identify the image of the face using SVMs classifier as well as the neural network are used as a classifier to compare its results with the proposed system. For a more comprehensive comparison, two face image databases (Yale and ORL) are used to test the performance of the system. Finally, the experimental results show the efficiency and reliability of face recognition system, and the results demonstrate accuracy on two databases indicated that the results enhancement 5% using the SVM classifier with polynomial Kernel function compared to use feedforward neural network classifier.

scholarly journals Comparison of principal component analysis algorithm and local binary pattern for feature extraction on face recognition system

2018 ◽  
Vol 197 ◽  
pp. 03001
Author(s):  
Ichsan Taufik ◽  
Maya Musthopa ◽  
Aldy Rialdy Atmadja ◽  
Muhammad Ali Ramdhani ◽  
Yana Aditia Gerhana ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Local Binary Pattern ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Recognition Time ◽  
Characteristic Extraction ◽  
Face Recognition System ◽  
Sample Test

Characteristic extraction in face recognition is a step to get characteristic information from the image. The characteristic extraction algorithm is tested against several scenarios of different sunlight and lights, objects facing the camera and not facing the camera. The sample test data were performed on 4 people using a video file or frame numbering 70 for recognizable faces using Principal Component Analysis (PCA) and Local Binary Pattern (LBP) algorithms. The result of the research shows that Local Binary Pattern (LBP) algorithm in object scenario facing camera with sunlighting in room has accuracy of 98.59%, recognition time of 812,817 milliseconds, FAR of 1,41% and FRR of 0%, while at Principal Component Analysis (PCA) 98.59% accuracy, recognition time of 1275,761 milliseconds, FAR of 1.41% and FRR of 0%. Based on these results, the Local Binary Pattern (LBP) algorithm is more efficient than Principal Component Analysis (PCA) for face recognition of the scenarios to be implemented in real-time video.

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