Jaringan Saraf Tiruan Backpropagation untuk Pengenalan Wajah Metode Ekstraksi Fitur Berbasis Histogram - Sigit Kusmaryanto (Jurnal EECCIS, 2014)
One common weakness in pattern recognition for face recognition is imperative that accurate input pattern to the pattern identified . This causes the input is often not recognized or not identified so as to be in the input repeatedly . The application of face recognition using Artificial Neural Network (ANN) backpropagation with MATLAB 7.0 is expected to overcome the weakness of pattern recognition systems for face recognition . Histogram -based feature extraction methods used in this study to obtain identification characteristics of the face image and a neural network input data . Face image data using pixel size variations . Trained in facial image pixel size variations 640 x 480 pixels and 600 x 800 pixels with two of distance making on face: average( 2-5m ) , close( < 2m ) . The results obtained from ANN test using 18 images with frontal face : resulting number of units in the hidden layer 6 , the number of input unit 255 , the number of output unit 10 , the maximum epoch 2500, 0001 and learning the target error rate = 0.9 with a percentage of 95 % of face recognitionKeyword - Backpropagation , Face Recognition Feature Extraction , Histogram .