Gaussian multiscale aggregation oriented to hand biometric segmentation in mobile devices

Abstract

Abstract-New trends in biometrics are oriented to mobile devices in order to increase the overall security in daily actions like bank account access, e-commerce or even document protection within the mobile.However, applying biometrics to mobile devices imply challenging aspects in biometric data acquisition, feature extraction or private data storage. Concretely, this paper attempts to deal with the problem of hand segmentation given a picture of the hand in an unknown background, requiring an accurate result in terms of hand isolation.For the sake of user acceptability, no restrictions are done on background, and therefore, hand images can be taken without any constraint, resulting segmentation in an exigent task.Multiscale aggregation strategies are proposed in order to solve this problem due to their accurate results in unconstrained and complicated scenarios, together with their properties in time performance. This method is evaluated with a public synthetic database with 480000 images considering different backgrounds and illumination environments.The results obtained in terms of accuracy and time performance highlight their capability of being a suitable solution for the problem of hand segmentation in contact-less environments, outperforming competitive methods in literature like Lossy Data Compression image segmentation (LDC).

Automatic usability and stress analysis in mobile biometrics

“…Therefore the migration of biometrics to these scenarios has become an important topic nowadays. There are several published works focusing on the adaptation of biometrics to mobile devices, using different modalities such as the iris [8], hand [9] or fingerprint [10]. In our previous works with mobile devices and dynamic handwritten signature recognition [11], the algorithm applied was tested under different conditions but the evaluation of its usability was left for a future work, being covered by this paper.…”