A MULTI-AGENT MODEL FOR FACE RECOGNITION USING MULTI-FEATURS AND MULTI-CLASSIFIERS
Fahmy, A., Salama, G., Elrahim, A., Elbar, M. (2008). A MULTI-AGENT MODEL FOR FACE RECOGNITION USING MULTI-FEATURS AND MULTI-CLASSIFIERS. EKB Journal Management System, 6(6th International Conference on Electrical Engineering ICEENG 2008), 1-14. doi: 10.21608/iceeng.2008.34360
Aly A. Fahmy; Gouda I. Salama; Alaa A. Elrahim; Magdy A. Elbar. "A MULTI-AGENT MODEL FOR FACE RECOGNITION USING MULTI-FEATURS AND MULTI-CLASSIFIERS". EKB Journal Management System, 6, 6th International Conference on Electrical Engineering ICEENG 2008, 2008, 1-14. doi: 10.21608/iceeng.2008.34360
Fahmy, A., Salama, G., Elrahim, A., Elbar, M. (2008). 'A MULTI-AGENT MODEL FOR FACE RECOGNITION USING MULTI-FEATURS AND MULTI-CLASSIFIERS', EKB Journal Management System, 6(6th International Conference on Electrical Engineering ICEENG 2008), pp. 1-14. doi: 10.21608/iceeng.2008.34360
Fahmy, A., Salama, G., Elrahim, A., Elbar, M. A MULTI-AGENT MODEL FOR FACE RECOGNITION USING MULTI-FEATURS AND MULTI-CLASSIFIERS. EKB Journal Management System, 2008; 6(6th International Conference on Electrical Engineering ICEENG 2008): 1-14. doi: 10.21608/iceeng.2008.34360

A MULTI-AGENT MODEL FOR FACE RECOGNITION USING MULTI-FEATURS AND MULTI-CLASSIFIERS

The International Conference on Electrical Engineering
Article 100, Volume 6, 6th International Conference on Electrical Engineering ICEENG 2008, May 2008, Page 1-14  XML PDF (327.92 K)
Document Type: Original Article
DOI: 10.21608/iceeng.2008.34360
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Authors
Aly A. Fahmy1; Gouda I. Salama2; Alaa A. Elrahim2; Magdy A. Elbar2
1Professor, Dean of the faculty of computers and information, Cairo University.
2Egyptian Armed Forces.
Abstract
Abstract:
This paper presents a new model based on multi-agent technology for face recognition
using multi-features and multi-classifiers. The human faces are verified by projecting
face images onto a feature space that spans the significant variations among known
faces by computing the discrete cosine transform (DCT) and discrete wavelet
transform (DWT) features. The classifiers used in this research namely, K-nearest
neighbor (K-NN), neural network (NN), support vector machine (SVM), BayesNet,
classification and regression tree (CART), and decision tree algorithm (C4.5). The
experimental results using these classifiers individually show that the recognition rate
is up to 95% on the Olivetti Research Laboratory (ORL) database of facial images
[14]. To improve the performance of the model, the classifier with the highest
recognition rate is correlated with other classifiers to select the most suitable
complementary group of classifiers that give a high recognition rate. Each classifier in
the group is represented by agent in a multi-agent system. An average of 97%
recognition rate is reached using K-NN, NN, and CART. Again, to improve the
performance of the model, each classifier in the agents group is applied on the DCT
feature vector and if the recognized face is not matched with the personal information
database then it is applied on the DWT feature vector. The experimental results
showed that the recognition rate using this model is up to 99.5%.
Keywords
Multi-Agent; Face Recognition; Multi-features; Multi-classifiers
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