Welcome to the InfoSci Platform
Reference Hub7
Reliable Face Recognition Using Artificial Neural Network

Reliable Face Recognition Using Artificial Neural Network

Shaimaa A. El-said
Copyright: © 2013 |Volume: 2 |Issue: 2 |Pages: 29
ISSN: 2160-9772|EISSN: 2160-9799|EISBN13: 9781466632899|DOI: 10.4018/ijsda.2013040102
Cite Article Cite Article

MLA

Shaimaa A. El-said. "Reliable Face Recognition Using Artificial Neural Network." IJSDA vol.2, no.2 2013: pp.14-42. http://doi.org/10.4018/ijsda.2013040102

APA

Shaimaa A. El-said. (2013). Reliable Face Recognition Using Artificial Neural Network. International Journal of System Dynamics Applications (IJSDA), 2(2), 14-42. http://doi.org/10.4018/ijsda.2013040102

Chicago

Shaimaa A. El-said. "Reliable Face Recognition Using Artificial Neural Network," International Journal of System Dynamics Applications (IJSDA) 2, no.2: 14-42. http://doi.org/10.4018/ijsda.2013040102

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Facial detection and recognition are among the most heavily researched fields of computer vision and image processing. Most of the current face recognition techniques suffer when the noises affect the global features or the local intensity pixels of the images under consideration. In the proposed Reliable Face Recognition System (RFRS) system, for the first time, a combination of Gabor Filter (GF), Principal component analysis (PCA) for efficient feature extraction, and ANN for classification is employed. This demonstrates how to detect faces in noisy images by training the network several times on various input; ideal and noisy images of faces. Applying GF before PCA reduces PCA sensitivity to noise, provides a greater level of invariance, and trains the ANN on different sets of noisy images. The output of the ANN is a vector whose length equal to the distinct subjects in Olivetti Research Laboratory (ORL). The ANN is trained to output a 1 in the correct position of the output vector and to fill the rest of the output vector with 0’s. Experimentation is carried out on RFRS by using ORL datasets. The experimental results show that training the network on noisy input images of face greatly reduce its errors when it had to classify or recognize noisy images. For noisy face images, the network did not make any errors for faces with noise of mean 0.00 or 0.05, while the average recognition rate varies from 96.8% to 98%. When noise of mean 0.10 is added to the images the network begins to make errors. For noiseless face images, the proposed system achieves correct classification. Performance comparison between RFRS and other face recognition techniques shows that for most of the cases, RFRS performs better than other conventional techniques under different types of noises and it shows the high robustness of the proposed algorithm.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.