Face Recognition using PCA Gobor Filter SVM Techniques
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International Journal of Computer & Organization Trends (IJCOT) |  |
| © 2016 by IJCOT Journal | ||
| Volume - 6 Issue - 3 |
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| Year of Publication : 2016 | ||
| Authors : Anju, Anu Rani | ||
| DOI : 10.14445/22492593/IJCOT-V34P306 |
Citation
Anju, Anu Rani "Face Recognition using PCA Gobor Filter SVM Techniques", International Journal of Computer & organization Trends (IJCOT), V6(3):20-23 May - Jun 2016, ISSN:2249-2593, www.ijcotjournal.org. Published by Seventh Sense Research Group.
Abstract Face recognition is the hot research topic from last few years but still it has become so difficult and large problem. The face recognition is the modulus operandi that human performs in their daily lives. The main challenges faced by the researchers are variation caused due to different expression and pose. The main feature that can used to extract the features from variant images that are caused because of different variations is Gabor Wavelets. Face recognition is the process of identification of a person by their facial image. This technique makes it possible to use their facial image of person to authenticate him into a secure system. In this thesis we have analysed the face images and non-face images using Gabor filter and SVM. In this we have created a training set using the eigen faces and the eigen faces are formed by using the Principle Component Analysis. The Principle Component Analysis face recognition algorithm is the one of the most important technique used for recognizing faces. The PCA is the technique that effectively and efficiently represents pictures of faces into its eigen faces components and these eigen face components form eigen faces these eigen faces are the ghost images of original images.The significant feature known as eigen faces don’t necessarily correspond to features such as eyes, ears and noses. It provides the ability to learn and later recognizes new faces in an unsupervised manner.
References
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Keywords
SVM, PCM, Face recognition.