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Biometric authentication using mobile devices is becoming a convenient and important means to secure access to remote services such as tele-banking 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.
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