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 PDF (327.92 K) | ||||
Document Type: Original Article | ||||
DOI: 10.21608/iceeng.2008.34360 | ||||
View on SCiNiTO | ||||
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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