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Keystroke Verification Challenge (KVC): Biometric and Fairness Benchmark Evaluation | IEEE Journals & Magazine | IEEE Xplore

Keystroke Verification Challenge (KVC): Biometric and Fairness Benchmark Evaluation


In the top row, the graphs show the performance of TypeNet (experiment 5F against experiment 10F) in the desktop case. In the bottom row, TypeFormer (experiment 5F agains...

Abstract:

Analyzing keystroke dynamics (KD) for biometric verification has several advantages: it is among the most discriminative behavioral traits; keyboards are among the most c...Show More

Abstract:

Analyzing keystroke dynamics (KD) for biometric verification has several advantages: it is among the most discriminative behavioral traits; keyboards are among the most common human-computer interfaces, being the primary means for users to enter textual data; its acquisition does not require additional hardware, and its processing is relatively lightweight; and it allows for transparently recognizing subjects. However, the heterogeneity of experimental protocols and metrics, and the limited size of the databases adopted in the literature impede direct comparisons between different systems, thus representing an obstacle in the advancement of keystroke biometrics. To alleviate this aspect, we present a new experimental framework to benchmark KD-based biometric verification performance and fairness based on tweet -long sequences of variable transcript text from over 185,000 subjects, acquired through desktop and mobile keyboards, extracted from the Aalto Keystroke Databases. The framework runs on CodaLab in the form of the Keystroke Verification Challenge (KVC). Moreover, we also introduce a novel fairness metric, the Skewed Impostor Ratio (SIR), to capture inter - and intra -demographic group bias patterns in the verification scores. We demonstrate the usefulness of the proposed framework by employing two state-of-the-art keystroke verification systems, TypeNet and TypeFormer, to compare different sets of input features, achieving a less privacy-invasive system, by discarding the analysis of text content (ASCII codes of the keys pressed) in favor of extended features in the time domain. Our experiments show that this approach allows to maintain satisfactory performance.
In the top row, the graphs show the performance of TypeNet (experiment 5F against experiment 10F) in the desktop case. In the bottom row, TypeFormer (experiment 5F agains...
Published in: IEEE Access ( Volume: 12)
Page(s): 1102 - 1116
Date of Publication: 21 December 2023
Electronic ISSN: 2169-3536

Funding Agency:

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Cites in Papers - |

Cites in Papers - IEEE (2)

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1.
Roberto Daza, Luis F. Gomez, Julian Fierrez, Aythami Morales, Ruben Tolosana, Javier Ortega-Garcia, "DeepFace-Attention: Multimodal Face Biometrics for Attention Estimation With Application to e-Learning", IEEE Access, vol.12, pp.111343-111359, 2024.
2.
Marco Huber, Anh Thi Luu, Naser Damer, "Recognition Performance Variation Across Demographic Groups Through the Eyes of Explainable Face Recognition", 2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG), pp.1-10, 2024.

Cites in Papers - Other Publishers (2)

1.
Marco Huber, Fadi Boutros, Naser Damer, "Frequency Matters: Explaining Biases of Face Recognition in the Frequency Domain", Computer Vision – ECCV 2024 Workshops, vol.15644, pp.279, 2025.
2.
Mohamed Meselhy Eltoukhy, Tarek Gaber, Abdulwahab Ali Almazroi, Marwa F. Mohamed, "ONE3A: one-against-all authentication model for smartphone using GAN network and optimization techniques", PeerJ Computer Science, vol.10, pp.e2001, 2024.

References

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