How Unique Is a Face: An Investigative Study

Balážia, Michal; Happy, S L; Brémond, François; Dantcheva, Antitza

doi:10.1109/icpr48806.2021.9412446

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…The ranking has been also used for grouping users demonstrated for image data according to their easiness of recognition [6]. A uniqueness measure score based on the Kullback-Leibler divergence has been described to quantify the identification capacity of faces by investigating the impact of feature extractors of deep neural network (DNN) algorithms [16]. Similar to the other biometric types, image performance variation is often related to the capturing devices or sensors, to the environment conditions, or to the user.…”

Section: Menagerie Modelsmentioning

confidence: 99%

A flexible approach for biometric menagerie on user classification of keystroke data

Özbek

2023

Journal of Electrical Engineering

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Biometric systems aim to provide reliable authentication and verification of users. The behaviour of the users may alter the authentication performance when accessing these systems. Therefore, clustering users based on their actions is crucial. A biometric menagerie defines and labels user groups statistically according to their variability. However, determining groups is a fuzzy process and it may lead to inconsistencies. In this work, a novel and flexible approach is introduced based on the classification performance of the users data collected in a database without imposing any other restrictions. According to the performance measures obtained from the confusion matrix of the classification algorithms, users are ranked and then clustered. Additionally, the norm of a confusion matrix is offered augmenting the state-of-the-art performance metrics. The proposed scheme is evaluated using the behavioural biometrics modality on two benchmark keystroke databases. The performance results successfully illustrate the alternative way of grouping and identification of users sharing the same behaviour irrespective of the chosen classifiers or performance metrics.

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Section: Menagerie Modelsmentioning

confidence: 99%