Elderly Signature Identification Using HOG Method and K-Nearest Neighbors Classifier

Abstract

Signature is used to legally approve an agreement, treaty, and state administrative activities. Identification of the signature is needed to ensure the ownership of a signature so that things do not happen that harm the owner of the signature, such as forgery. In this study, the authors identified signatures for people over 50 years of age. The obtained signature is scanned to produce a signature image, the image is pre-processed so that it is ready for feature extraction that can characterize the signature image. The HOG method is used to perform feature extraction to produce a dataset that has 4,536 feature vectors for each signature image. The K-NN classification method is used to identify the signature image, the highest accuracy is obtained when using the value of K = 3, 4 which is 98.6%. The value of K = 7, 8, 10 obtained the lowest accuracy value of 96%. The decrease in accuracy from the highest to the lowest which is not significant shows that the dataset from feature extraction using the HOG method can characterize one signature ownership with other signatures quite well. The HOG method obtains a large feature vector for each signature, so it is recommended to optimize the feature extraction to reduce the feature size and speed up computing.