BibTex Citation Data :
@article{BFIS4978, author = {Tri Mulyono and Kusworo Adi and Rahmat Gernowo}, title = {SISTEM PENGENALAN WAJAH DENGAN METODE EIGENFACE DAN JARINGAN SYARAF TIRUAN (JST)}, journal = {BERKALA FISIKA}, volume = {15}, number = {1}, year = {2012}, keywords = {}, abstract = { The development of security systems led to the development of face recognition system using image processing techniques.Research was conducted to identify a face image automatically with theeigenface method. The method used is a normalization, eigenface, neural network training and testing.Eigenface is used to reduce the dimension vector face becomes much simpler (eigen vector). Eigen vectorsobtained are used by back propagation neural network training process and recognition. Then do thetesting process using the image of a face that has not been used in the training process. The results showed the use of neural networks and eigenface to face recognition can give a goodaccuracy. The system is able to produce an acuracy of 84.6% with a FAR (False Acceptance Rate) =16.2%, FRR (False Rejection Rate) = 20% and EER (Equal Error Rate) = 0.3. Keywords : face recognition, eigenface, eigen vector, neural network }, pages = {15--20} url = {https://ejournal.undip.ac.id/index.php/berkala_fisika/article/view/4978} }
Refworks Citation Data :
The development of security systems led to the development of face recognition system using image processing techniques.Research was conducted to identify a face image automatically with theeigenface method. The method used is a normalization, eigenface, neural network training and testing.Eigenface is used to reduce the dimension vector face becomes much simpler (eigen vector). Eigen vectorsobtained are used by back propagation neural network training process and recognition. Then do thetesting process using the image of a face that has not been used in the training process.
The results showed the use of neural networks and eigenface to face recognition can give a goodaccuracy. The system is able to produce an acuracy of 84.6% with a FAR (False Acceptance Rate) =16.2%, FRR (False Rejection Rate) = 20% and EER (Equal Error Rate) = 0.3.
Keywords : face recognition, eigenface, eigen vector, neural network
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Last update: 2024-11-05 10:24:17
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