Component-connected Feature for Signature Identification

Abstract

Abstract-A signature is the oldest security techniques to verify the identification of a person. This is due to every person has a different signature, and each signature has the characteristic physiological and behavior. There are two kinds of signature such as offline and online signatures used to verify someone identity. Offline signatures were used in this study because offline signature does not have dynamic features such as an online signature. This study proposed an identification system of offline signature by using k-NN based on the features that were stored in the database. The proposed identification system consists of preprocessing, feature extraction and verification stages. We collected the data samples from 10 persons. Each person wrote ten signatures. Total data was 100 signatures. The first stage used in this study was preprocessing such as noise removal, binarization, skeleton, and cropping. The second stage was feature extraction. Feature extraction had some vital information such as height-width ratio, the ratio of the density of signatures, edge distance ratio, the ratio of the number and proximity of the column, and the number of connected components in the signature. That information was stored in a separate database. We separated ten signatures of each person into six signatures as data sample and four signature as test data. We verified 40 signatures of test data from 10 persons using k-NN. It is shown that from 40 signatures used in our test data, 28 signatures were correctly identified and 12 signatures belong to others.

A Performance of Combined Methods of VCG and 16BCD for Feature Extraction on HSV

“…Reference [6] proposed an algorithm to correct the alignment of the input signature which can be used at the pre-processing stage to achieve better results in the signature detection process. Several methods of extracting statistical features have also discussed in [7][8][9][10][11][12]. Reference [13] had used Gabor Filter to extract the surf feature and critical point matching, while the classification is based on Hidden Markov Model (HMM).…”