Human Face Recognition Using Superior Principal Component Analysis (SPCA)

2010 ◽  
pp. 688-691 ◽  
Author(s):  
Arjun V. MANE ◽  
Ramesh R. MANZA ◽  
Karbhari V KALE
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Human Face ◽  
Human Face Recognition

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.

scholarly journals Comparison of Different Face Recognition Method Based On PCA

2014 ◽  
Vol 10 (4) ◽  
pp. 2016-2022
Author(s):  
Rajib Saha ◽  
Debotosh Bhattacharjee ◽  
Sayan Barman
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Manhattan Distance ◽  
Human Face ◽  
Recognition Method ◽  
Human Face Recognition ◽  
Face Matching ◽  
Chebychev Distance

This paper is about human face recognition in image files. Face recognition involves matching a given image with the database of images and identifying the image that it resembles the most. Here, face recognition is done using: (a) Eigen faces and (b) applying Principal Component Analysis (PCA) on image. The aim is to successfully demonstrate the human face recognition using Principal component analysis & comparison of Manhattan distance, Eucleadian distance & Chebychev distance for face matching.

scholarly journals Human face recognition methods based on principle component analysis (PCA), wavelet and support vector machine (SVM) : a comparative study

2020 ◽  
Vol 20 (2) ◽  
pp. 991
Author(s):  
Eman A. Gheni ◽  
Zahraa M. Algelal
Keyword(s):  
Face Recognition ◽  
Wavelet Transforms ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Performance Comparison ◽  
Support Vector ◽  
Human Face ◽  
Human Face Recognition ◽  
Feature Extracting

<p class="JESTECAbstract">Human face Recognition systems are increasingly gaining more importance and can be utilized throughout many applications like video surveillance, Security, human-computer intelligent interaction, etc. this paper presents performance comparison between three feature extraction techniques for an automatic face recognition system. In the first step, we benefit from wavelet Transforms, Principal Component Analysis (PCA) and combining Wavelet with PCA as feature extracting methods. After feature vectors generation, linear and nonlinear Support Vector Machines (SVM) are usually used for implementing the classification or recognition step. These methods are compared on accuracy in an ORL database for face recognition applications including 400 images of 40 people.</p>

Sign in / Sign up

Export Citation Format

Share Document