Improving Handwritten Signature-Based Identity Prediction through the Integration of Fuzzy Soft-Biometric Data

2012 ◽  
Author(s):  
Marjory da Costa-Abreu ◽  
Michael Fairhurst
Keyword(s):  
Biometric Data ◽  
Handwritten Signature

scholarly journals Behavioral Biometric Data Analysis for Gender Classification Using Feature Fusion and Machine Learning

2021 ◽  
Vol 8 ◽  
Author(s):  
Shivanand S. Gornale ◽  
Sathish Kumar ◽  
Abhijit Patil ◽  
Prakash S. Hiremath
Keyword(s):  
Machine Learning ◽  
Feature Fusion ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Gender Classification ◽  
Biometric Data ◽  
Security Applications ◽  
Biometric Security ◽  
Learning Techniques ◽  
Handwritten Signature

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.

scholarly journals An evaluation of a three-modal hand-based database to forensic-based gender recognition

2019 ◽  
Author(s):  
Juliana De A. S. M. ◽  
Márjory Da Costa-Abreu
Keyword(s):  
The Other ◽  
Gender Recognition ◽  
Performance Information ◽  
Biometric Data ◽  
Soft Biometrics ◽  
Biometric Systems ◽  
Handwritten Signature

In recent years, behavioural soft-biometrics have been widely used to improve biometric systems performance. Information like gender, age and ethnicity can be obtained from more than one behavioural modality. In this paper, we propose a multimodal hand-based behavioural database for gender recognition. Thus, our goal in this paper is to evaluate the performance of the multimodal database. For this, the experiment was realised with 76 users and was collected keyboard dynamics, touchscreen dynamics and handwritten signature data. Our approach consists of compare two-modal and one-modal modalities of the biometric data with the multimodal database. Traditional and new classifiers were used and the statistical Kruskal-Wallis to analyse the accuracy of the databases. The results showed that the multimodal database outperforms the other databases.

Secure Dynamic Signature-Crypto Key Computation

2010 ◽  
pp. 368-384
Author(s):  
Andrew Teoh Beng Jin ◽  
Yip Wai Kuan
Keyword(s):  
Social Acceptance ◽  
The Other ◽  
Key Generation ◽  
Minimal Level ◽  
Biometric Data ◽  
Advanced Society ◽  
Feature Discretization ◽  
Dynamic Signature ◽  
Handwritten Signature ◽  
Discretization Process

Biometric-key computation is a process of converting a piece of live biometric data into a key. Among the various biometrics available today, the hand signature has the highest level of social acceptance. The general masses are familiar with the use of handwritten signature by means of verification and acknowledgement. On the other hand, cryptography is used in multitude applications present in technologically advanced society. Examples include the security of ATM cards, computer networks, and e-commerce. The signature crypto-key computation is hence of highly interesting as it is a way to integrate behavioral biometrics with the existing cryptographic framework. In this chapter, we report a dynamic hand signatures-key generation scheme which is based on a randomized biometric helper. This scheme consists of a randomized feature discretization process and a code redundancy construction. The former enables one to control the intraclass variations of dynamic hand signatures to the minimal level and the latter will further reduce the errors. Randomized biometric helper ensures that a signature-key is easy to be revoked when the key is compromised. The proposed scheme is evaluated based on the 2004 signature verification competition (SVC) database. We found that the proposed methods are able to produce keys that are stable, distinguishable, and secure.

scholarly journals Handwritten Signature Biometric Data Analysis for Personality Prediction System Using Machine Learning Techniques

2021 ◽  
Vol 9 (5) ◽  
pp. 1-22
Author(s):  
Shivanand S. Gornale ◽  
Sathish Kumar ◽  
Prakash S. Hiremath
Keyword(s):  
Recognition Rate ◽  
The State ◽  
Machine Learning Techniques ◽  
Prediction System ◽  
Biometric Data ◽  
Behavioral Traits ◽  
Learning Techniques ◽  
Personality Prediction ◽  
Different Characteristics ◽  
Handwritten Signature

Handwritten signature has been considered as one of the most widely accepted behavioral personal trait in Biometric security system; and  It contains various dynamic and innate behavioral traits like shapes and patterns which can certainly find a person’s soft characteristics like age, gender, Personality, handedness and many more. Person’s signature or handwriting determines the state of the person’s mind or personality characteristics at the time of writing. This paper provides a personality prediction system of different characteristics determining the personality of a person based on offline handwritten signature Images. Experiments are carried out using supervised learning techniques. Results shows a significant recognition rate and validates the effectiveness against the state-of-art techniques in comparison to similar works.

Audiovisual Expression of Biometric Data Based on the Polysomnography Test

2018 ◽  
Vol 5 (4) ◽  
pp. 1-5
Author(s):  
Na Yea Oh ◽  
Hee Soo Kim ◽  
Jin Wan Park
Keyword(s):  
Biometric Data

GEOMETRIC VISUALIZATION OF THE PROBLEM OF MEDICAL DIAGNOSTICS BIOMETRIC DATA OF THE BIOSENSOR PLATFORM

2018 ◽  
Vol 41 (4) ◽  
pp. 37-39 ◽  
Author(s):  
Igor I. Koltunov ◽  
Anton V. Panfilov ◽  
Ivan A. Poselsky ◽  
Nikolay N. Chubukov ◽  
Ivan V. Krechetov ◽  
...  
Keyword(s):  
Medical Diagnostics ◽  
Biometric Data
Sign in / Sign up

Export Citation Format

Share Document