Abstract
Biometric security applications have been employed for providing a higher security in several access control systems during the past few years. The handwritten signature is the most widely accepted behavioral biometric trait for authenticating the documents like letters, contracts, wills, MOU’s, etc. for validation in day to day life. In this paper, a novel algorithm to detect gender of individuals based on the image of their handwritten signatures is proposed. The proposed work is based on the fusion of textural and statistical features extracted from the signature images. The LBP and HOG features represent the texture. The writer’s gender classification is carried out using machine learning techniques. The proposed technique is evaluated on own dataset of 4,790 signatures and realized an encouraging accuracy of 96.17, 98.72 and 100% for k-NN, decision tree and Support Vector Machine classifiers, respectively. The proposed method is expected to be useful in design of efficient computer vision tools for authentication and forensic investigation of documents with handwritten signatures.
Highlights
Days, Biometrics is a widely established measure in security and authentication processes in several system applications
The fusion of different features were tested over the signature database and it was noted that, by fusing HOG and local binary patterns (LBP) features, the highest accuracy of 97.87% is achieved by Decision Tree, and the lowest accuracy of 94.53% obtained by K-NN classifier
With the fusion of LBP and Statistical features, the highest accuracy of 96.20% is achieved by Decision Tree, and the lowest accuracy of 82.25% obtained by K-NN classifier
Summary
Biometrics is a widely established measure in security and authentication processes in several system applications. According to International Standard Organization (ISO), biometric is defined as a means of biological process for recognizing and analyzing an individual based on their physiological (fingerprint, face, iris, palm prints) and behavioral characteristics (Signature, Gaits, keystroke, voice/speech) (Fairhurst et al, 2017). Among these traits, handwritten signature is the most preferred age-old behavioral trait, which is used to authenticate the documents due to its individuality and consistence features. The objective of the proposed work is to design a machine learning framework for analyzing the gender classification of writers based on their handwritten signatures using fusion of textural and statistical features. To bridge this research gap, a frame work is proposed to identify writer’s gender from handwritten signatures of individuals with varying ages
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