Biometric authentication using mobile devices is becoming a convenient and important means to secure access to remote services such as telebanking and electronic transactions. Such an application poses a very challenging pattern recognition problem: the training samples are often sparse and they cannot represent the biometrics of a person. The query features are easily affected by the acquisition environment, the user’s accessories, occlusions and aging. Semi-supervised learning – learning from the query/test data – can be a means to tap the vast unlabeled training data. While there is evidence that semi-supervised learning can work in text categorization and biometrics, its application on mobile devices remains a great challenge. As a preliminary, yet, indispensable study towards the goal of semi-supervised learning, we analyze the following sub-problems: model adaptation, update criteria, inference with several models and user-specific time-dependent performance assessment, and explore possible solutions and research directions.
Scheda prodotto non validato
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo
|Titolo:||Challenges and Research Directions for Adaptive Biometric Recognition Systems|
|Data di pubblicazione:||2009|
|Citazione:||Challenges and Research Directions for Adaptive Biometric Recognition Systems / POH N; WONG R; KITTLER J; ROLI F. - 5558(2009), pp. 753-764. ((Intervento presentato al convegno ICB 2009 tenutosi a Alghero, Italy, nel June 2-5, 2009..|
|Tipologia:||4.1 Contributo in Atti di convegno|