P339 - BIOSIG 2023 - Proceedings of the 22nd International Conference of the Biometrics Special Interest Group
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Auflistung P339 - BIOSIG 2023 - Proceedings of the 22nd International Conference of the Biometrics Special Interest Group nach Schlagwort "Biometric performance measurement"
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- KonferenzbeitragDEFT: A new distance-based feature set for keystroke dynamics(BIOSIG 2023, 2023) Kaluarachchi, Nuwan; Kandanaarachchi, Sevvandi; Moore, Kristen; Arakala, ArathiKeystroke dynamics is a behavioural biometric utilised for user identification and authentication. We propose a new set of features based on the distance between keys on the keyboard, a concept that has not been considered before in keystroke dynamics. We combine flight times, a popular metric, with the distance between keys on the keyboard and call them as Distance Enhanced Flight Time features (DEFT). This novel approach provides comprehensive insights into a person’s typing behaviour, surpassing typing velocity alone. We build a DEFT model by combining DEFT features with other previously used keystroke dynamic features. The DEFT model is designed to be device-agnostic, allowing us to evaluate its effectiveness across three commonly used devices: desktop, mobile, and tablet. The DEFT model outperforms the existing state-of-the-art methods when we evaluate its effectiveness across two datasets. We obtain accuracy rates exceeding 99% and equal error rates below 10% on all three devices.
- KonferenzbeitragExploring the Untapped Potential of Unsupervised Representation Learning for Training Set Agnostic Finger Vein Recognition(BIOSIG 2023, 2023) Tugce Arican, Raymond VeldhuisFinger vein patterns are a promising biometric trait because of their higher privacy and security features compared to face and finger prints. Finger vein recognition methods have been researched extensively, especially deep learning based methods such as Convolutional Neural Networks. These methods show promising recognition performance, but their low degree of generalization and adaptability results in much lower and inconsistent recognition performance in cross database scenarios. Despite these drawbacks, much less research has gone into the generalization and adaptability of these deep learning methods. This study addresses these issues and proposes an unsupervised learning approach, namely a patch-based Convolutional Auto-encoder for learning finger vein representations. Our proposed approach outperforms traditional baseline finger recognition methods on the UTFVP, SDUMLA-HMT, and PKU datasets, and achieves state-of-the-art performance on the UTFVP dataset with 0.24\% EER. It also indicates a noticeably higher generalization of finger vein features across different datasets compared to a supervised method. The findings of this work offer promising advancements in achieving robust finger vein recognition in real-life scenarios, due to the enhanced generalization and adaptability of our proposed method.
- KonferenzbeitragA RISE-based explainability method for genuine and impostor face verification(BIOSIG 2023, 2023) Naima Bousnina, Joao AscensoHeat Map (HM)-based explainable Face Verification (FV) has the goal to visually interpret the decision-making of black-box FV models. Despite the impressive results, state-of-the-art FV explainability methods based on HMs mainly address genuine verification by generating visual explanations that reveal the similar face regions which most contributed for acceptance decisions. However, the similar face regions may not be the unique critical regions for the model decision, notably when rejection decisions are performed. To address this issue, this paper proposes a more complete FV explainability method, providing meaningful HM-based explanations for both genuine and impostor verification and associated acceptance and rejection decisions. The proposed method adapts the RISE algorithm for FV to generate Similarity Heat Maps (S-HMs) and Dissimilarity Heat Maps (D-HMs) which offer reliable explanations to all types of FV decisions. Qualitative and quantitative experimental results show the effectiveness of the proposed FV explainability method beyond state-of-the-art benchmarks.
- KonferenzbeitragStatistical Methods for Testing Equity of False Non Match Rates across Multiple Demographic Groups(BIOSIG 2023, 2023) Michael Schuckers, Kaniz FatimaBiometric recognition is used for a variety of applications including authentication, identity proofing, and border security. One recent focus of research and development has been methods to ensure fairness across demographic groups and metrics to evaluate fairness. However, there has been little work in this area incorporating statistical variation. This is important because differences among groups can be found by chance when no difference is present or may be due to an actual difference in system performance. We extend previous work to consider when individuals are members of one or more demographics (age, gender, race). Our methodology is meant to be more comprehendable by a non-technical audience and uses a robust bootstrap approach for estimation of variation in false non-match rates. After presenting our methodology, we present a simulation study and we apply our approach to MORPH-II data.