Implementasi Ekstraksi Fitur Jumlah Keypoint Descriptor Pada Pengenalan Tanda Tangan Dengan Algoritme Learning Vector Quantization - Imada Nur Afifah; Wayan Firdaus Mahmudy (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Signature recognition is important for signature verification process. One of the signature recognition method is implementation Learning Vector Quantization (LVQ) for signature recognition with additional method of features extraction using Scale Invariant Features Transform (SIFT). In the train process, this research used some features such as maximum of black pixel in horizontal and vertical histogram, center of mass, normalized area of signature, aspect ratio, tri surface feature, the Six Fold Surface feature, transition feature and additional features called number of keypoints. Number of keypoints are output of Scale Invariant Features Transform (SIFT) method. The dataset used is 100 images for training data and 100 images for testing data from 20 different classes. And 25 images from out of trained class as unknown data. The result of algorithm testing is 71,2% from testing of non-threshold process, 56% from testing process with maximum value of minimum euclidean distance between data and class as threshold value, 45,6% % from testing process with second maximum value of minimum euclidean distance between data and class as threshold value.