On Multiview Analysis for Fingerprint Liveness Detection

Fingerprint recognition systems, as any other biometric system, can be subject to attacks, which are usually carried out using artificial fingerprints. Several approaches to discriminate between live and fake fingerprint images have been presented to address this issue. These methods usually rely on the analysis of individual features extracted from the fingerprint images. Such features represent different and complementary views of the object in analysis, and their fusion is likely to improve the classification accuracy. However, very little work in this direction has been reported in the literature. In this work, we present the results of a preliminary investigation on multiview analysis for fingerprint liveness detection. Experimental results show the effectiveness of such approach, which improves previous results in the literatur

Related standards

This chapter reports about the relevant international standardization activities in the field of biometrics and describes a harmonized taxonomy for terms in the field of liveness detection. The scope and progress of the presentation attack detection standard ISO/IEC 30107 is discussed

Activity-Based Sleep-Wake Identification in Infants

Actigraphy offers one of the best known alternatives to polysomnography for wake-sleep identification. The advantages of actigraphy include high accuracy, simplicity of use and low intrusiveness. These features allow of use actigraphy for determining wake-sleep states in such highly sensitive groups as infants. Both logistic regression and neural networks were tested as predictors. The accuracy of predicted wake-sleep states were established in comparison to the sleep/wake states recorded by technicians in a polysomnograph study. Both prediction methods provided good accuracy of prediction and agreement in results with other studies, thus validating the suggested methodology. 1