Biometrics has burst into mobile technology. Fingerprint scanners are being embedded in smartphones and tablets supplying these devices with the security and usability provided by biometric authentication mechanisms. However, performance results obtained by biometric systems cannot be extrapolated to mobile devices. The conditions change, especially at capture process, due to the reduced sensing area of the scanners used. The impact of small fingerprint scanners on the quality and biometric performance of the system is studied. A database using three different fingerprint scanners has been collected and reduced-size images (i.e. 12 × 12 mm 2 , 10 × 10 mm 2 and 8 × 8 mm 2) have been modelled by cropping the original ones. Performance testing has been conducted using one public and one commercial algorithm, and considering two application scenarios. One scenario in which enrolment and authentication are executed using the same small sensor included in the mobile device (i.e. cropped image against cropped image) and a second scenario in which enrolment is executed using an external larger sensor and authentication is done using the mobile device sensor (i.e. full image against cropped image). Results show the gradual worsening of quality and error rates as the size of the fingerprint scanner is reduced revealing a significant difference between the application scenarios analysed.

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-Nowadays biometrics is being used in many applications where security is required. This fact causes that new threatens have appeared and that the number of attempts to break biometric systems has increased. From all potential attacks, those involving damage or thefts to users are the most worrying. Most of them could be avoided if acquisition sensors would have suitable approaches for aliveness detection at the capture process. Many providers claim that their products support these methods but unfortunately it has been discovered that some products do not detect fake samples. In this paper a methodology based on Common Criteria is given to evaluate, in an independent way, whether biometric capture devices implement methods for fake samples detection, and till which extent such methods are effective. This methodology has been tested with sensors from different modalities.

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