BioTouchPass: Handwritten Passwords for Touchscreen Biometrics

@article{Tolosana2020BioTouchPassHP,
  title={BioTouchPass: Handwritten Passwords for Touchscreen Biometrics},
  author={Rub{\'e}n Tolosana and Rub{\'e}n Vera-Rodr{\'i}guez and Julian Fierrez},
  journal={IEEE Transactions on Mobile Computing},
  year={2020},
  volume={19},
  pages={1532-1543}
}
This work enhances traditional authentication systems based on Personal Identification Numbers (PIN) and One-Time Passwords (OTP) through the incorporation of biometric information as a second level of user authentication. In our proposed approach, users draw each digit of the password on the touchscreen of the device instead of typing them as usual. A complete analysis of our proposed biometric system is carried out regarding the discriminative power of each handwritten digit and the… 

BioTouchPass Demo: Handwritten Passwords for Touchscreen Biometrics

BioTouchPass enhances traditional authentication systems based on Personal Identification Numbers (PIN) and One-Time Passwords (OTP) through the incorporation of biometric information from handwriting as a second level of user authentication by providing a user-friendly interface easily adaptable to a variety of mobile devices and application scenarios.

BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networks

The proposed TA-RNN system outperforms the state of the art, achieving a final 2.38% Equal Error Rate, using just a 4-digit password and one training sample per character, in comparison with traditional typed-based password systems.

MobileTouchDB: Mobile Touch Character Database in the Wild and Biometric Benchmark

The database contains more than 64K on-line character samples performed by 217 users, using 94 different smartphone models, with an average of 314 samples per user, providing an easily reproducible framework for two different scenarios of biometric user authentication.

SelfiePass: A Shoulder Surfing Resistant Graphical Password Scheme

  • S. RajarajanP. Priyadarsini
  • Computer Science
    2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)
  • 2021
A novel scheme for entering the click points on images without allowing shoulder surfing attacks is proposed and an android implementation of the proposed scheme was used to verify the usability and security of the proposal.

Do You Need More Data? The DeepSignDB On-Line Handwritten Signature Biometric Database

The main contribution of this study is to present and describe the new DeepSignDB on-line handwritten signature biometric public database and to propose a standard experimental protocol and benchmark to be used for the research community in order to perform a fair comparison of novel approaches with the state of the art.

MSDS: A Large-Scale Chinese Signature and Token Digit String Dataset for Handwriting Verification

Surprisingly, verification performances of state-of-the-art methods on MSDS-TDS are generally better than those on MS DS-ChS, which indicates that the handwritten Token Digit String could be a more effective biometric than handwritten Chinese signature.

Evaluation of Deep Learning Models for Person Authentication Based on Touch Gesture

This work investigates the ability of Deep Learning to automatically discover useful features of touch gesture and use them to authenticate the user in dynamic touch authentication system.

Swipe Dynamics as a Means of Authentication: Results From a Bayesian Unsupervised Approach

Results are presented from a set of experiments consisting of 38 sessions with labelled ‘victim’ as well as blind and over-the-shoulder presentation attacks and Bayesian unsupervised models are presented as they are well suited to such conditions.

Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health

The importance of considering security, health, and metadata from a joint perspective is remarked, especially critical due to the risks inherent when using these behavioral signals.

Online User Authentication System Using Keystroke Dynamics

This work presents a novel authentication approach based on two factors: password and KSD, and proposes a prototype for a keyboard in order to collect timing and non-timing information from KSDs.

References

SHOWING 1-10 OF 46 REFERENCES

Incorporating Touch Biometrics to Mobile One-Time Passwords: Exploration of Digits

This work evaluates the advantages and potential of incorporating touch biometrics to mobile one-time passwords (OTP). The new e-BioDigit database, which comprises online handwritten numerical digits

Benchmarking Touchscreen Biometrics for Mobile Authentication

The results show various new insights into the distinctiveness of swipe interaction, e.g., some gestures hold more user-discriminant information, data from landscape orientation is more stable, and horizontal gestures are more discriminative in general than vertical ones.

User verification using safe handwritten passwords on smartphones

This article extracts and select 25 static and dynamic biometric features from a handwritten character password sequence on an android touch-screen device and uses the classification algorithms of WEKA framework for writer verification using safe handwritten passwords on smartphones.

Improving Accuracy, Applicability and Usability of Keystroke Biometrics on Mobile Touchscreen Devices

This paper presents the first reported study on mobile keystroke biometrics which compares touch-specific features between three different hand postures and evaluation schemes, and shows that including spatial touch features reduces implicit authentication equal error rates (EER) by 26.4 - 36.8% relative to the previously used temporal features.

Synchronous One Time Biometrics with Pattern Based Authentication

A new protocol combining protected biometric data and a classical synchronous one time password is proposed to enhance the security of user authentication while preserving usability and privacy.

One-time password for biometric systems: disposable feature templates

Improved extraction technique coupled with the feature selection technique has an improved identification performance compared with the traditional genetic based extraction approach and the features are shown to be unique enough to determine a replay attack is occurring, compared with a more traditional feature extraction technique.

Multitouch Gesture-Based Authentication

Results indicate that biometric information gleaned from a short user-device interaction remains consistent across gaps of several days, though there is noticeable degradation of performance when the authentication is performed over multiple sessions.

Performance evaluation of handwritten signature recognition in mobile environments

One of the big challenges of this research was to discover if the handwritten signature modality in mobile devices should be split into two different modalities, one for those cases when the signature is performed with a stylus, and another when the fingertip is used for signing.

Exploring Touch-Screen Biometrics for User Identification on Smart Phones

An application for the Android mobile platform is developed to collect data on the way individuals draw lock patterns on a touchscreen, which achieves an average Equal Error Rate (EER) of approximately 10.39%, meaning that lock patterns biometrics can be used for identifying users towards their device, but could also pose a threat to privacy if the users’ biometric information is handled outside their control.

Presentation Attacks in Signature Biometrics: Types and Introduction to Attack Detection

Results obtained for both BiosecurID and e-BioSign databases show the high impact on the system performance regarding not only the level of information that the attacker has but also the training and effort performing the signature.