BioTouchPass: Handwritten Passwords for Touchscreen Biometrics

• URL: http://arxiv.org/abs/2205.01353v1
• Date: Tue, 3 May 2022 07:42:47 GMT
• Title: BioTouchPass: Handwritten Passwords for Touchscreen Biometrics
• Authors: Ruben Tolosana, Ruben Vera-Rodriguez and Julian Fierrez
• Abstract summary: This work enhances traditional authentication systems based on Personal Identification Numbers (PIN) and One-Time Passwords (OTP) 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 robustness when increasing the length of the password and the number of enrolment samples.
• Score: 3.867363075280544
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 robustness when increasing the length of the password and the number of enrolment samples. The new e-BioDigit database, which comprises on-line handwritten digits from 0 to 9, has been acquired using the finger as input on a mobile device. This database is used in the experiments reported in this work and it is available together with benchmark results in GitHub. Finally, we discuss specific details for the deployment of our proposed approach on current PIN and OTP systems, achieving results with Equal Error Rates (EERs) ca. 4.0% when the attacker knows the password. These results encourage the deployment of our proposed approach in comparison to traditional PIN and OTP systems where the attack would have 100% success rate under the same impostor scenario.

Related papers

• Multi-Channel Time-Series Person and Soft-Biometric Identification [65.83256210066787]
  This work investigates person and soft-biometrics identification from recordings of humans performing different activities using deep architectures. We evaluate the method on four datasets of multi-channel time-series human activity recognition (HAR) Soft-biometric based attribute representation shows promising results and emphasis the necessity of larger datasets.
  arXiv  Detail & Related papers  (2023-04-04T07:24:51Z)
• Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation, and COTS Evaluation [63.05890836038913]
  We study the role of soft biometrics to enhance person recognition systems in unconstrained scenarios. We consider two assumptions: 1) manual estimation of soft biometrics and 2) automatic estimation from two commercial off-the-shelf systems. Experiments are carried out fusing soft biometrics with two state-of-the-art face recognition systems based on deep learning.
  arXiv  Detail & Related papers  (2022-10-24T11:29:57Z)
• Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learning [0.0]
  This study contributes to the research being done on behavioral biometrics by creating and evaluating a user authentication scheme using behavioral biometrics. The behavioral biometrics used in this study include touch dynamics and phone movement. We evaluate the performance of different single-modal and multi-modal combinations of the two biometrics.
  arXiv  Detail & Related papers  (2022-05-07T05:16:34Z)
• Mobile Behavioral Biometrics for Passive Authentication [65.94403066225384]
  This work carries out a comparative analysis of unimodal and multimodal behavioral biometric traits. Experiments are performed over HuMIdb, one of the largest and most comprehensive freely available mobile user interaction databases. In our experiments, the most discriminative background sensor is the magnetometer, whereas among touch tasks the best results are achieved with keystroke.
  arXiv  Detail & Related papers  (2022-03-14T17:05:59Z)
• Writer Recognition Using Off-line Handwritten Single Block Characters [59.17685450892182]
  We use personal identity numbers consisting of the six digits of the date of birth, DoB. We evaluate two recognition approaches, one based on handcrafted features that compute directional measurements, and another based on deep features from a ResNet50 model. Results show the presence of identity-related information in a piece of handwritten information as small as six digits with the DoB.
  arXiv  Detail & Related papers  (2022-01-25T23:04:10Z)
• Biometrics: Trust, but Verify [49.9641823975828]
  Biometric recognition has exploded into a plethora of different applications around the globe. There are a number of outstanding problems and concerns pertaining to the various sub-modules of biometric recognition systems.
  arXiv  Detail & Related papers  (2021-05-14T03:07:25Z)
• Multimodal Personal Ear Authentication Using Smartphones [0.0]
  fingerprint authentication cannot be used when hands are wet, and face recognition cannot be used when a person is wearing a mask. We examine a personal authentication system using the pinna as a new approach for biometric authentication on smartphones.
  arXiv  Detail & Related papers  (2021-03-23T14:19:15Z)
• Mind the GAP: Security & Privacy Risks of Contact Tracing Apps [75.7995398006171]
  Google and Apple have jointly provided an API for exposure notification in order to implement decentralized contract tracing apps using Bluetooth Low Energy. We demonstrate that in real-world scenarios the GAP design is vulnerable to (i) profiling and possibly de-anonymizing persons, and (ii) relay-based wormhole attacks that basically can generate fake contacts.
  arXiv  Detail & Related papers  (2020-06-10T16:05:05Z)
• End-to-end User Recognition using Touchscreen Biometrics [11.394909061094463]
  The goal was to create an end-to-end system that can transparently identify users using raw data from mobile devices. In the proposed system data from the touchscreen goes directly to the input of a deep neural network, which is able to decide on the identity of the user.
  arXiv  Detail & Related papers  (2020-06-09T16:38:09Z)
• Analyze and Development System with Multiple Biometric Identification [0.0]
  Methods developed earlier to ensure personal the information from the thefts was not effective and safe. Biometrics were introduced when it was needed technology for more efficient security of personal information. In biometric authentication system, user may not remember any passwords or carry any keys.
  arXiv  Detail & Related papers  (2020-04-10T05:08:54Z)
• BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networks [6.5379404287240295]
  This work enhances password scenarios through two-factor authentication asking the users to draw each character of the password instead of typing them as usual. We present the novel MobileTouchDB public database, acquired in an unsupervised mobile scenario with no restrictions in terms of position, posture, and devices. In addition, we present a novel approach named Time-Aligned Recurrent Neural Networks (TA-RNNs)
  arXiv  Detail & Related papers  (2020-01-28T09:25:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.