Flooding-based segmentation for contactless hand biometrics oriented to mobile devices

Segmentation is a crucial stage in hand biometric recognition due to its direct influence on the feature extraction process. The actual trend toward contactless biometrics adds new challenges to traditional defiances, which are mainly related to the capturing conditions and the limitations on computational resources. Traditional methods do not succeed when variable capturing conditions are imposed and methods which are able to deal with daily-life situations are, in general, computationally expensive. In this study, a competitive flooding-based segmentation method oriented to mobile devices is proposed in order to achieve a compromised solution between accuracy and computational resources consumption. The method has been evaluated using images coming from five different databases which cover a wide spectrum of capturing conditions, one of them recorded as a part of this study. The results have been compared with other two well known segmentation techniques in terms of both accuracy and computation time.