Face recognition could be applied to a variety of practical applications and problems, including security and criminal identification systems. Face recognition using eigenface approach was motivated by information theory as it provides a practical solution. In this paper, the Principal Component Analysis (PCA) is used for eigenfaces (eigenvectors) computation. These eigenfaces present the extracted features for the faces to be recognized. A multilayer Artificial Neural Network (ANN) with back propagation adaptive learning algorithm is used for the classification phase. A number of experiments have been conducted on the system using the Olivetti Research Laboratory (ORL) database. Promising results have been achieved. Total performance accuracy on the data set used reached 98%.
Shaarawy, M., Ismail, H., & Hassanain, K. (1998). A STATISTICAL CONNECTIONIST APPROACH FOR FACE RECOGNITION. The International Conference on Electrical Engineering, 1(1st International Conference on Electrical Engineering ICEENG 1998), 325-334. doi: 10.21608/iceeng.1998.61088
MLA
M. Shaarawy; H. Ismail; K. Hassanain. "A STATISTICAL CONNECTIONIST APPROACH FOR FACE RECOGNITION". The International Conference on Electrical Engineering, 1, 1st International Conference on Electrical Engineering ICEENG 1998, 1998, 325-334. doi: 10.21608/iceeng.1998.61088
HARVARD
Shaarawy, M., Ismail, H., Hassanain, K. (1998). 'A STATISTICAL CONNECTIONIST APPROACH FOR FACE RECOGNITION', The International Conference on Electrical Engineering, 1(1st International Conference on Electrical Engineering ICEENG 1998), pp. 325-334. doi: 10.21608/iceeng.1998.61088
VANCOUVER
Shaarawy, M., Ismail, H., Hassanain, K. A STATISTICAL CONNECTIONIST APPROACH FOR FACE RECOGNITION. The International Conference on Electrical Engineering, 1998; 1(1st International Conference on Electrical Engineering ICEENG 1998): 325-334. doi: 10.21608/iceeng.1998.61088
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