Framework for Biometric User Authentication Based on a Dynamic Handwritten Signature

In the article, a method for cryptographic keys generation and a person's biometric verification based on a fuzzy extractor mechanism is considered. The method uses 19 face biometric features. The method's scheme is presented, and all its essential steps are described in details. A comprehensive investigation of the method was conducted. The operability of the method was demonstrated. Suggested method allows one to generate cryptographic keys up to 232 bits. An average share of necessary redundancy for encoded key information in relation to the original information is about 52%. An optimal configuration of the method with 13 and 14 biometric features and 49% redundancy was discovered.

Generation of Cryptographic Keys and Person's Verification Based on face Biometric

In this paper we proposed the approach for recognition handwritten signatures based on dynamics characteristics and fuzzy sets theory. We suggested the formal model of human handwritten signature, which contains several fuzzy features. We also suggested handwritten signature reference template creation and handwritten signature recognition algorithms. Suggested formal model was used as a basis. Method of potentials was used to obtain membership functions of fuzzy features which makes it possible to create a reference template even under conditions of a small capacity of training set. The research was conducted on the MCYT_Signature_100 signature collection which includes 2500 genuine signatures and 2500 skillful fakes. Suggested approach makes it possible to recognize handwritten signatures even in case of their fuzzy character. Accuracy evaluation results showed FAR value 2.8%, FRR value 0.4% for random fake patterns and FRR value 0.8% for skillful fake patterns, which is better than evaluation results of many others approaches.

E. S. Anisimova

Papers

Fuzzy Sets Theory Approach for Recognition Handwritten Signatures