Accuracy Improvement of Face Recognition System based on Co-occurrence Matrix of Local Median Binary Pattern

2019 ◽  
Author(s):  
Rana Jelokhani Niaraki ◽  
Asadollah Shahbahrami
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Accuracy Improvement ◽  
Face Recognition System ◽  
Occurrence Matrix

scholarly journals Face Recognition Approach Based on the Integration of Image Preprocessing, CMLABP and PCA Methods

2017 ◽  
Vol 13 (1) ◽  
pp. 104-113
Author(s):  
Yaqeen Mezaal
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Input Image ◽  
Birth Date ◽  
Image Preprocessing ◽  
Face Recognition System ◽  
Proposed Model ◽  
Occurrence Matrix ◽  
Local Average

Face recognition technique is an automatic approach for recognizing a person from digital images using mathematical interpolation as matrices for these images. It can be adopted to realize facial appearance in the situations of different poses, facial expressions, ageing and other changes. This paper presents efficient face recognition model based on the integration of image preprocessing, Co-occurrence Matrix of Local Average Binary Pattern (CMLABP) and Principle Component Analysis (PCA) methods respectively. The proposed model can be used to compare the input image with existing database images in order to display or record the citizen information such as name, surname, birth date, etc. The recognition rate of the model is better than 99%. Accordingly, the proposed face recognition system is functional for criminal investigations. Furthermore, it has been compared with other reported works in the literature using diverse databases and training images.

scholarly journals Face Recognition Approach Based on the Integration of Image Preprocessing, CMLABP and PCA Methods

2017 ◽  
Vol 13 (1) ◽  
pp. 104-113
Author(s):  
Yaqeen Mezaal
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Input Image ◽  
Birth Date ◽  
Image Preprocessing ◽  
Face Recognition System ◽  
Proposed Model ◽  
Occurrence Matrix ◽  
Local Average

Face recognition technique is an automatic approach for recognizing a person from digital images using mathematical interpolation as matrices for these images. It can be adopted to realize facial appearance in the situations of different poses, facial expressions, ageing and other changes. This paper presents efficient face recognition model based on the integration of image preprocessing, Co-occurrence Matrix of Local Average Binary Pattern (CMLABP) and Principle Component Analysis (PCA) methods respectively. The proposed model can be used to compare the input image with existing database images in order to display or record the citizen information such as name, surname, birth date, etc. The recognition rate of the model is better than 99%. Accordingly, the proposed face recognition system is functional for criminal investigations. Furthermore, it has been compared with other reported works in the literature using diverse databases and training images.

scholarly journals The Using of PCA, Wavelet and GLCM In Face Recognition System, A Comparative Study

2018 ◽  
Vol 26 (10) ◽  
pp. 131-139 ◽  
Author(s):  
Manar Abdulkaream Al-Abaji ◽  
Meaad Mohammed Salih
Keyword(s):  
Face Recognition ◽  
Euclidean Distance ◽  
Principal Component ◽  
Recognition System ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Reduction Techniques ◽  
System A ◽  
Face Recognition System ◽  
Occurrence Matrix

The process of data dimension reduction plays an important role in any  face recognition system because many of these data are repetitive and irrelevant and this cause a problem in applications of data mining and learning the machine. The main purpose is to improve the performance of recognition by eliminating repetitive features.           In this research, a number of data reduction techniques were used like: Principal Component Analysis, Gray-Level Co-occurrence Matrix and Discrete Wavelet Transform for extracting the most important features from the images of persons. A different number of training and testing images were used to compare the performance of each of the techniques above in the recognition process. Euclidean distance scale was used to get results.  

scholarly journals Design of face recognition system based on CNN

2020 ◽  
Vol 1601 ◽  
pp. 052011
Author(s):  
Yong Li ◽  
Zhe Wang ◽  
Yang Li ◽  
Xu Zhao ◽  
Hanwen Huang
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Face Recognition System

HUMAN FACE RECOGNITION FROM A SINGLE FRONT VIEW

1992 ◽  
Vol 06 (04) ◽  
pp. 571-593 ◽  
Author(s):  
CHING-WEN CHEN ◽  
CHUNG-LIN HUANG
Keyword(s):  
Face Recognition ◽  
Active Contour Model ◽  
Facial Feature ◽  
Recognition System ◽  
Facial Features ◽  
Human Face Recognition ◽  
Front View ◽  
Deformable Template ◽  
Face Recognition System ◽  
Feature Values

This paper presents a face recognition system which can identify the unknown identity effectively using the front-view facial features. In front-view facial feature extractions, we can capture the contours of eyes and mouth by the deformable template model because of their analytically describable shapes. However, the shapes of eyebrows, nostrils and face are difficult to model using a deformable template. We extract them by using the active contour model (snake). After the contours of all facial features have been captured, we calculate effective feature values from these extracted contours and construct databases for unknown identities classification. In the database generation phase, 12 models are photographed, and feature vectors are calculated for each portrait. In the identification phase if any one of these 12 persons has his picture taken again, the system can recognize his identity.

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