Face recognition system using Multilinear Principal Component Analysis and Locality Preserving Projection

2011 ◽  
Author(s):  
J. Shermina
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Locality Preserving Projection ◽  
Locality Preserving ◽  
Face Recognition System ◽  
Multilinear Principal Component Analysis

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.

scholarly journals Sistem Kontrol Akses Berbasis Real Time Face Recognition dan Gender Information

2015 ◽  
Vol 6 (2) ◽  
pp. 198
Author(s):  
Putri Nurmala ◽  
Wikaria Gazali ◽  
Widodo Budiharto
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Real Time ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Identification System ◽  
Face Recognition System ◽  
Gender Information ◽  
And Gender

Face recognition and gender information is a computer application for automatically identifying or verifying a person's face from a camera to capture a person's face. It is usually used in access control systemsand it can be compared to other biometrics such as finger print identification system or iris. Many of face recognition algorithms have been developed in recent years. Face recognition system and gender information inthis system based on the Principal Component Analysis method (PCA). Computational method has a simple and fast compared with the use of the method requires a lot of learning, such as artificial neural network. In thisaccess control system, relay used and Arduino controller. In this essay focuses on face recognition and gender - based information in real time using the method of Principal Component Analysis ( PCA ). The result achievedfrom the application design is the identification of a person’s face with gender using PCA. The results achieved by the application is face recognition system using PCA can obtain good results the 85 % success rate in face recognition with face images that have been tested by a few people and a fairly high degree of accuracy.

scholarly journals Multiscale Retinex Application to Analyze Face Recognition

2020 ◽  
Vol 5 (2) ◽  
pp. 217
Author(s):  
Supriyanto Supriyanto ◽  
Maisevli Harika ◽  
Maya Sri Ramadiani ◽  
Diena Rauda Ramdania
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Facial Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Facial Image ◽  
Main Challenge ◽  
Face Recognition System

The main challenge that facial recognition introduces is the difficulty of uneven lighting or dark tendencies. The image is poorly lit, which makes it difficult for the system to perform facial recognition. This study aims to normalize the lighting in the image using the Multiscale Retinex method. This method is applied to a face recognition system based on Principal Component Analysis to determine whether this method effectively improves images with uneven lighting. The results showed that the Multiscale Retinex approach to face recognition's correctness was better, from 40% to 76%. Multiscale Retinex has the advantage of dark facial image types because it produces a brighter image output.

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.

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