Face Recognition Using Gray Level Weight Matrix (GLWM)

2011 ◽  
pp. 69-72
Author(s):  
R. S. Sabeenian ◽  
M. E. Paramasivam ◽  
P. M. Dinesh
Keyword(s):  
Face Recognition ◽  
Weight Matrix ◽  
Gray Level

scholarly journals Face Identification on Login Security Using Algorithm Combination of Viola-Jones and Cosine Similarity

2020 ◽  
Vol 4 (1) ◽  
pp. 203-211
Author(s):  
FADHILLAH AZMI ◽  
Amir Saleh ◽  
N P Dharshinni
Keyword(s):  
Face Recognition ◽  
Data Security ◽  
Image Matching ◽  
Cosine Similarity ◽  
Facial Features ◽  
Gray Level ◽  
Image Detection ◽  
Texture Characteristics ◽  
Occurrence Matrix ◽  
Different Characteristics

Data security by using an alphanumeric combination password is no longer used, so it needs to be added security that is difficult to be manipulated by certain people. One type of security is the type of biometrics technology using face recognition which has different characteristics by combining the Viola-Jones algorithm to detect facial features, GLCM (Gray Level Co-occurrence Matrix) for extracting the texture characteristics of an image, and Cosine Similarity for the measurement of the proximity of the data (image matching). The image will be detected using the Viola-Jones algorithm to get face, eyes, nose, and mouth. The image detection results will be calculated the value of the texture characteristics with the GLCM (Gray Level Cooccurrence Matrix) algorithm. Image matching using cosine similarity will determine or match the data stored in the database with new image input until identification results are obtained. The results obtained in this study get the level of accuracy of the identification of the three algorithms by 77.20% with the amount of data that was correctly identified as many as 386 out of 500 images.Keywords: Security, face recognition, Viola-Jones, Cosine Similarity.

scholarly journals Occlusion invariant face recognition using mean based weight matrix and support vector machine

Sadhana ◽  
2014 ◽  
Vol 39 (2) ◽  
pp. 303-315 ◽  
Author(s):  
G NIRMALA PRIYA ◽  
R S D WAHIDA BANU
Keyword(s):  
Support Vector Machine ◽  
Face Recognition ◽  
Weight Matrix ◽  
Support Vector

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 A Proposed Generalized Eigenfaces System for Face Recognition Based on One Training Image

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Shaimaa Khudhair Salah ◽  
Waleed Rasheed Humood ◽  
Ahmed Othman Khalaf
Keyword(s):  
Face Recognition ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Training Image ◽  
Training Data ◽  
Gray Level ◽  
Analysis Algorithm ◽  
Front End ◽  
Biological Treatments ◽  
Components Analysis

This paper discusses the results of a study that aimed to develop an eigenface technique known as (PC) 2A that collect the image of the original face with its vertical and horizontal projections. The basic components of the image were analyzed in the image enrichment section. An evaluation of the proposed method demonstrates that it costs less than the standard eigenface technique. Moreover, the experimental results show that a front-end database that has a gray level for each person has one training image; thus, in terms of accuracy, it was possible to get a 3-5% result for the proposed (PC)2A, which is higher than the precision of the standard eigenface technique. The main objective of this paper is to demonstrate the weaknesses and strengthens of the facial recognition approach as an identifier known as eigenfaces. This aim was achieved by using the principal components analysis algorithm based on the images of previously stored training data. The outcomes show the strength of the proposed technique, in which it was possible to obtain accuracy results of up to 96%, which in turn provides support for developing the technique proposed in this paper in the future because this work is of great importance in the field of biological treatments, the need for which has significantly increased over the last 5 years.

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