Face Recognition using Eigenvector and Principle Component Analysis

@article{Chakraborty2012FaceRU,
  title={Face Recognition using Eigenvector and Principle Component Analysis},
  author={Dulal Chakraborty and Sanjit Kumar Saha and Md. Al-Amin Bhuiyan},
  journal={International Journal of Computer Applications},
  year={2012},
  volume={50},
  pages={42-49}
}
Face recognition is an important and challenging field in computer vision. [] Key Method Various symmetrization techniques are used for preprocessing the image in order to handle bad illumination and face alignment problem. We used Eigenface approach for face recognition. Eigenfaces are eigenvectors of covariance matrix, representing given image space. Any new face image can then be represented as a linear combination of these Eigenfaces. This makes it easier to match any two given images and thus face…

Figures and Tables from this paper

Towards Face Recognition Using Eigenface
TLDR
Experimental results indicate that the proposed eigenface-based approach can classify the faces with accuracy more than 80% in all cases.
Face Identification Based on Discrete Wavelet Transform and Neural Networks
TLDR
A side-view face authentication approach based on discrete wavelet transform and artificial neural networks for the solution of the problem of face identification is proposed.
Realization and optimization of face recognition system based on MATLAB
TLDR
In this dissertation, a face recognition system has been built using MATLAB, after the Eigenface has been achieved, two techniques to deal with illumination changing have been implemented, shadow compensation and local binary pattern (LBP) respectively.
International Journal of Emerging Technologies in Computational and Applied Sciences ( IJETCAS ) www . iasir
TLDR
The proposed method shows a significant improvement in terms of PSNR and MSE as compared to other existing denoising algorithms on ultrasound and MRI images.
APLIKASI PENGENALAN WAJAH MENGGUNAKAN METODE EIGENFACE DAN JARAK EUCLIDEAN
Wajah merupakan salah satu identitas bagi setiap individu pada sistem biometrik. Wajah merupakan ciri unik dari setiap manusia yang dapat membedakan rupa antar manusia. Berbeda dengan manusia yang
The Face of the Robots.
Face Recognition Analysis Using PCA, ICA And Neural Network
TLDR
The face recognition system based on PCA-ICA and Neural Network has been developed and its performance has been compared with traditional PCA -ICA method.
Side View Face Identification Based on Wavelet and Random Forest
TLDR
A subset selection method that increases the number of training samples and allows subsets to preserve the global information is presented, which takes the advantage of wavelet's localization property in both frequency and spatial domains.

References

SHOWING 1-10 OF 24 REFERENCES
A feature based approach to face recognition
A feature-based approach to face recognition in which the features are derived from the intensity data without assuming any knowledge of the face structure is presented. The feature extraction model
Eigenfaces for Recognition
TLDR
A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
Human face profile recognition by computer
The Automatic Recognition of Human Faces from Profile Silhouettes
TLDR
A pattern recognition system is described which is capable of identifying human faces from their full profile silhouettes and is compared to human observers with the result that the system performs no worse than the human observers.
Face recognition using a digital neural network with self-organising capabilities
  • M. A. Kerin, T. Stonham
  • Computer Science
    [1990] Proceedings. 10th International Conference on Pattern Recognition
  • 1990
TLDR
An approach to face recognition using neural networks is presented and all automatic training procedure based on self-organization is defined, demonstrating the system's facial recognition capabilities using multiple classes of data.
Automatic recognition of human face profiles
Feature extraction from faces using deformable templates
TLDR
De deformable templates are illustrated by showing their ability for tracking features and altering parameter values to minimize the energy function, thereby deforming itself to find the best fit.
Identification of human faces based on isodensity maps
Identification of human faces
TLDR
These studies form a foundation for continuing research on real-time man-machine interaction for computer classification and identification of multidimensional vectors specified by noisy components.
How Faces Differ—A New Comparative Technique
It can be argued that the process of recognising faces progresses in two stages: first, the realisation that a perceived image contains patterns that may most reasonably be interpreted as forming a
...
...