Sensor-enabled biometric signature-based authentication method for smartphone users. (9th January 2023)

• Record Type: Journal Article
• Authors: Ahmad, Sumaiya; Mishra, Sarthak; Zareen, Farhana Javed; Jabin, Suraiya
• Is Part Of: International journal of biometrics. Volume 15:Number 2(2023)
• Page Start: 212
• Page End: 232
• Publication Date: 2023-01-09
• Subjects: authentication -- biometrics -- smartphone sensors -- online biometric signatures -- recurrent neural network -- RNN -- bidirectional LSTM -- BiLSTM -- iSignDB; Biometric identification -- Periodicals; Biometry -- Periodicals; 570.15195
• Languages: English
• ISSNs: 1755-8301

Abstract

With the ubiquity of smartphones, the need for foolproof authentication mechanisms that support 'authentication on the go' and remote authentication is on the rise. Signature being the most sophisticated authentication method, this paper explores idea of designing signature based robust authentication method combined with smartphone sensor based features. We used our already created sensor enabled biometric signature database iSignDB which has recorded images of signatures along with associated smartphone sensor readings while signing on the touchscreen of the smartphone. Additionally, we considered six statistical features with the objective of designing a highly accurate authentication model. We successfully trained our authentication model over iSignDB using bidirectional LSTM method and obtained FAR 2%, FRR 12%, and EER 2% against skilled; and FAR 5%, FRR 12%, EER 6% against random forgery attacks respectively, which is comparable to the existing state-of-the-art models for smartphone signature biometrics. The main contribution of this work is extensive feature vector, and robust signature-based authentication method.