A framework for offline signature verification system: Best features selection approach

Mobile devices and technologies have become increasingly popular, offering comparable storage and computational capabilities to desktop computers allowing users to store and interact with sensitive and private information. The security and protection of such personal information are becoming more and more important since mobile devices are vulnerable to unauthorized access or theft. User authentication is a task of paramount importance that grants access to legitimate users at the point of entry and continuously through the usage session. This task is made possible with today’s smartphones’ embedded sensors that enable continuous and implicit user authentication by capturing behavioral biometrics and traits. In this article, we survey more than 140 recent behavioral biometric-based approaches for continuous user authentication, including motion-based methods (28 studies), gait-based methods (19 studies), keystroke dynamics-based methods (20 studies), touch gesture-based methods (29 studies), voice-based methods (16 studies), and multimodal-based methods (34 studies). The survey provides an overview of the current state-of-the-art approaches for continuous user authentication using behavioral biometrics captured by smartphones’ embedded sensors, including insights and open challenges for adoption, usability, and performance.

Sensor-Based Continuous Authentication of Smartphones’ Users Using Behavioral Biometrics: A Contemporary Survey

Mobile devices and technologies have become increasingly popular, offering comparable storage and computational capabilities to desktop computers allowing users to store and interact with sensitive and private information. The security and protection of such personal information are becoming more and more important since mobile devices are vulnerable to unauthorized access or theft. User authentication is a task of paramount importance that grants access to legitimate users at the point-of-entry and continuously through the usage session. This task is made possible with today's smartphones' embedded sensors that enable continuous and implicit user authentication by capturing behavioral biometrics and traits. In this paper, we survey more than 140 recent behavioral biometric-based approaches for continuous user authentication, including motion-based methods (28 studies), gait-based methods (19 studies), keystroke dynamics-based methods (20 studies), touch gesture-based methods (29 studies), voice-based methods (16 studies), and multimodal-based methods (34 studies). The survey provides an overview of the current state-of-the-art approaches for continuous user authentication using behavioral biometrics captured by smartphones' embedded sensors, including insights and open challenges for adoption, usability, and performance.

Sensor-based Continuous Authentication of Smartphones' Users Using Behavioral Biometrics: A Contemporary Survey

As the technology improves, there are many new innovations which give the security frameworks with different methods that are used to identify a person. Signature recognition is one of methods used to identify person. In this paper, offline signature recognition using back propagation neuron network system and image processing techniques has been proposed. Preprocessing of signature can be done using image processing techniques which involves RGB2Gray conversion, filtering, adjusting, thresholding followed by canny edge detection and at the last image scaling applied to reduce the processing time. Processed image feature is extracted using back propagation neuron network system with defined number of neurons and hidden layers. Similarly, data set images undergo preprocessing operation and features are extracted. Based on number of layers that are hidden and neuron, better recognition rate is obtained. The proposed method shows that the experimental result has more success rate.

https://link.springer.com/content/pdf/10.1007/s42979-021-00591-y.pdf

Offline Signature Recognition Using Image Processing Techniques and Back Propagation Neuron Network System

Identity verification based on the dynamic signature is an important issue of biometrics. There are many effective methods to the signature verification which take into account the dynamic characteristics of the signature (e.g. velocity of the pen, the pen’s pressure on the surface of the graphic tablet, etc.). Among these methods, the ones based on the so-called global features are very important. In our previous paper we have proposed new algorithm for evolutionary selection of the dynamic signature global features, which selects a subset of features individually for each user. Algorithm proposed in this paper is a faster version of the method proposed earlier. During development of the algorithm we resigned from using evolutionary selection of global features and standardized working of the classifier in the context of all users. The paper contains the simulation results for the BioSecure database of the dynamic signatures.

Prabir Ghosh

Papers

Offline Signature Verification Using Pixel Matching Technique