MATLAB Implementation of Face Identification Using Principal Component Analysis

2012 ◽  
Vol 433-440 ◽  
pp. 5402-5408
Author(s):  
Nasrul Humaimi Mahmood ◽  
Ismail Ariffin ◽  
Camallil Omar ◽  
Nur Sufiah Jaafar
Keyword(s):  
Principal Component Analysis ◽  
Minimum Distance ◽  
Principal Component ◽  
Component Analysis ◽  
Face Identification ◽  
Human Face ◽  
Face Images ◽  
The Face ◽  
Matlab Implementation ◽  
Pca Algorithm

Face is the greatest superior biometric as the face has a complex, multidimensional and meaningful identity compared from one person to another. Face identification is executed by comparing the characteristics of the face (test image) with those of known individual images in the database. This paper describes the used of the Principal Component Analysis (PCA) algorithm for human face identification based on webcam image. The MATLAB is used as a tool for image processing and analysis. The important decision to identify the person is by the minimum distance of the face images and known face images in face space. From the results, it can be concluded that the work has successfully implemented the PCA algorithm for human face identification through a webcam.

scholarly journals Half-face based recognition using principal component analysis

2021 ◽  
Vol 22 (3) ◽  
pp. 1404
Author(s):  
Ahmed M. Alkababji ◽  
Sara Raed Abd
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition Method ◽  
Face Images ◽  
The Face ◽  
Considerable Problem ◽  
Full Face ◽  
Pca Algorithm

<span lang="EN-US">Face recognition is a considerable problem in the field of image processing. It is used daily in various applications from personal cameras to forensic investigations. Most of the provides solutions proposed based on full-face images, are slow to compute and need more storage. In this paper, we propose an effective way to reduce the features and size of the database in the face recognition method and thus we get an increase in the speed of discrimination by using half of the face. Taking advantage of face symmetry, the first step is to divide the face image into two halves, then the left half is processed using the principal component analysis (PCA) algorithm, and the results are compared by using Euclidian distance to distinguish the person. The system was trained and tested on ORL database. It was found that the accuracy of the system reached up to 96%, and the database was minimized by 46% and the running time was decreased from 120 msec to 70 msec with a 41.6% reduction.</span>

scholarly journals Traffic Violation Detection using Principal Component Analysis and Viola Jones Algorithms

2019 ◽  
Vol 8 (3) ◽  
pp. 5549-5555
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Rule Violation ◽  
Face Features ◽  
The Face ◽  
Principal Component Analysis Algorithm ◽  
Traffic Violation ◽  
Pca Algorithm ◽  
Traffic Rule

This paper describes an application to detect traffic rule violation using principal component analysis algorithm(PCA).The proposed system will detect crowded bikes using PCA and Viola Johnson algorithms. The viola-Jones computation is seen as convincing in order to check and focus the face features. The face acknowledgment is strategy of perceiving region of face from a picture of one or different individuals together. The perceived face is removed in the proposed using the viola-Jones estimation. This application uses camera to recognize the amount of faces in the edge which identifies with number of people going in a bike. As indicated by the organization controls only two adults or two adults and one adolescent are permitted to go in a bike. We use Violo Johnes and PCA Algorithm to perceive the appearances to choose the amount of faces in the edge. Consequently the endeavor derives that through this structure we execute OCR to check the number plate to recognize the bike liberating with numerous people. This is a customized system to keep up a vital good ways from the accident by driving past the limited part on bike. At the point when our system perceives the over-trouble vehicle, the number plate of the vehicles is discovered using OCR.

Vision-Based Human Face Recognition Using Extended Principal Component Analysis

2013 ◽  
Vol 5 (4) ◽  
pp. 82-94 ◽  
Author(s):  
A. F. M. Saifuddin Saif ◽  
Anton Satria Prabuwono ◽  
Zainal Rasyid Mahayuddin ◽  
Teddy Mantoro
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Personal Identification ◽  
Human Face ◽  
Analysis Model ◽  
Human Face Recognition ◽  
Proposed Model ◽  
The Face

Face recognition has been used in various applications where personal identification is required. Other methods of person's identification and verification such as iris scan and finger print scan require high quality and costly equipment. The objective of this research is to present an extended principal component analysis model to recognize a person by comparing the characteristics of the face to those of new individuals for different dimension of face image. The main focus of this research is on frontal two dimensional images that are taken in a controlled environment i.e. the illumination and the background is constant. This research requires a normal camera giving a 2-D frontal image of the person that will be used for the process of the human face recognition. An Extended Principal Component Analysis (EPCA) technique has been used in the proposed model of face recognition. Based on the experimental results it is expected that proposed the EPCA performs well for different face images when a huge number of training images increases computation complexity in the database.

New Pattern Recognition Method Based on Wavelet De-Noising and Kernel Principal Component Analysis

2012 ◽  
Vol 235 ◽  
pp. 74-78 ◽  
Author(s):  
Jia Jun Zhang ◽  
Li Juan Liang
Keyword(s):  
Principal Component Analysis ◽  
Image Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Face Image ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Svm Classifier ◽  
Face Images ◽  
The Face

The background noise influences the face image recognition greatly. It is crucial to remove the noise signals prior to the face image recognition processing. For this purpose, the wavelet de-noising technology has combined with the kernel principal component analysis (KPCA) to identify face images in this paper. The wavelet de-noising technology was firstly used to remove the noise signals. Then the KPCA was employed to extract useful principal components for the face image recognition. By doing so, the dimensionality of the feature space can be reduced effectively and hence the performance of the face image recognition can be enhanced. Lastly, a support vector machine (SVM) classifier was used to recognize the face images. Experimental tests have been conducted to validate and evaluate the proposed method for the face image recognition. The analysis results have showed high performance of the newly proposed method for face image identification.

scholarly journals Patch-Based Principal Component Analysis for Face Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Tai-Xiang Jiang ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Tian-Hui Ma
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Nearest Neighbor ◽  
Spatial Information ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Face Images ◽  
Pca Method ◽  
Local Spatial Information

We have proposed a patch-based principal component analysis (PCA) method to deal with face recognition. Many PCA-based methods for face recognition utilize the correlation between pixels, columns, or rows. But the local spatial information is not utilized or not fully utilized in these methods. We believe that patches are more meaningful basic units for face recognition than pixels, columns, or rows, since faces are discerned by patches containing eyes and noses. To calculate the correlation between patches, face images are divided into patches and then these patches are converted to column vectors which would be combined into a new “image matrix.” By replacing the images with the new “image matrix” in the two-dimensional PCA framework, we directly calculate the correlation of the divided patches by computing the total scatter. By optimizing the total scatter of the projected samples, we obtain the projection matrix for feature extraction. Finally, we use the nearest neighbor classifier. Extensive experiments on the ORL and FERET face database are reported to illustrate the performance of the patch-based PCA. Our method promotes the accuracy compared to one-dimensional PCA, two-dimensional PCA, and two-directional two-dimensional PCA.

Making faces with computers

2005 ◽  
Vol 13 (3) ◽  
pp. 459-479 ◽  
Author(s):  
Graham Pike ◽  
Nicola Brace ◽  
Jim Turner ◽  
Sally Kynan
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Construction Process ◽  
Appropriate Use ◽  
Two Generations ◽  
The Face ◽  
Preliminary Study

Knowledge concerning the cognition involved in perceiving and remembering faces has informed the design of at least two generations of facial compositing technology. These systems allow a witness to work with a computer (and a police operator) in order to construct an image of a perpetrator. Research conducted with systems currently in use has suggested that basing the construction process on the witness recalling and verbally describing the face can be problematic. To overcome these problems and make better use of witness cognition, the latest systems use a combination of Principal Component Analysis (PCA) facial synthesis and an array-based interface. The present paper describes a preliminary study conducted to determine whether the use of an array-based interface really does make appropriate use of witness cognition and what issues need to be considered in the design of emerging compositing technology.

scholarly journals Improved Approach for Identification of Real and Fake Smile using Chaos Theory and Principal Component Analysis

2019 ◽  
Vol 54 (5) ◽  
Author(s):  
Hayder Ansaf ◽  
Hayder Najm ◽  
Jasim Mohammed Atiyah ◽  
Oday A. Hassen
Keyword(s):  
Principal Component Analysis ◽  
Chaos Theory ◽  
Principal Component ◽  
Component Analysis ◽  
Human Beings ◽  
Multiple Parameters ◽  
Detection Approach ◽  
The Face ◽  
And Performance ◽  
Smile Detection

The smile detection approach is quite prominent with the face detection and thereby the enormous implementations are prevalent so that the higher degree of accuracy can be achieved. The face smile detection is widely associated to have the forensic of faces of human beings so that the future predictions can be done. In chaos theory, the main strategy is to have the cavernous analytics on the single change and then to predict the actual faces in the analysis. In addition, the integration of Principal Component Analysis (PCA) is integrated to have the predictions with more accuracy. This work proposes to use the analytics on the parallel integration of PCA and chaos theory to enable the face smile and fake identifications to be made possible. The projected work is analyzed using assorted parameters and it has been found that the deep learning integration approach for chaos and PCA is quite important and performance aware in the multiple parameters with the different datasets in evaluations.

scholarly journals Exploiting Wavelet Transform, Principal Component Analysis, Support Vector Machine, and K-Nearest Neighbors for Partial Face Recognition

2019 ◽  
Vol 3 (2) ◽  
pp. 80-84 ◽  
Author(s):  
Mustafa H. Mohammed Alhabib ◽  
Mustafa Zuhaer Nayef Al-Dabagh ◽  
Firas H. AL-Mukhtar ◽  
Hussein Ibrahim Hussein
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Wavelet Transform ◽  
Principal Component ◽  
Component Analysis ◽  
Input Image ◽  
Personal Identification ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
The Face

Facial analysis has evolved to be a process of considerable importance due to its consequence on the safety and security, either individually or generally on the society level, especially in personal identification. The paper in hand applies facial identification on a facial image dataset by examining partial facial images before allocating a set of distinctive characteristics to them. Extracting the desired features from the input image is achieved by means of wavelet transform. Principal component analysis is used for feature selection, which specifies several aspects in the input image; these features are fed to two stages of classification using a support vector machine and K-nearest neighborhood to classify the face. The images used to test the strength of the suggested method are taken from the well-known (Yale) database. Test results showed the eligibility of the system when it comes to identify images and assign the correct face and name.

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