An overview on recent hand biometrics systems is presented in Table 1. This table presents the relation between the features required for identification, the method www.selleckchem.com/products/azd9291.html proposed, the population involved together with the results obtained, in terms of Equal Error Rate (EER).Table 1.Literature review on most recent works related to contact-less hand biometrics based on hand geometry. This table presents the relation between the features required for identification, the method proposed, the population involved together with the results …As hand biometrics tends to contact-less scenarios, hand image pre-processing increases in difficulty and laboriousness, since less constraints are required concerning background, i.e., the part behind the hand.
Several approaches in literature tackle with this problem by providing non-contact, platform-free scenarios but with constrained background, usually employing a monochromatic color, easily distinctive from hand texture [23]. More realistic environments propose a color-based segmentation, detecting hand-like pixels either based on probabilistic [16] or clustering methods [18,24]. Although, the constraints on background are less restrictive in this case, the performance of this segmentation procedure still lacks in accuracy.However, a feasible solution for this latter scenario is based on an acquisition involving short distance to sensor. This approach considers the use of infrared illumination [9,18], due to the fact that infrared light only lighten close-to-camera regions, avoiding further regions (background) to be illuminated and therefore not acquired by the infrared camera.
Most recent trends in hand segmentation consider no constraint on background, proposing more efficient approaches based on multiscale aggregation, providing promising results in real scenarios [24]. This scenario is clearly oriented to the application of hand biometrics in mobile devices.Moreover, hand biometrics also consider different acquisition modalities, namely 3D data acquisition Batimastat [14,25], infrared cameras [9,18], scanners [6] or low-resolution acquisition devices [10,13].Best results in Table 1 are achieved by Rahman et al. [26] and Kanhangad et al. [25]. The former work consists of applying Distance Based Nearest Neighbour (DBNN) and Graph Theory to both feature extraction and feature comparison.
In contrast, the latter work presents a new approach to achieve significantly Olaparib Sigma improved performance even in the presence of large hand pose variations, by estimating the orientation of the hands in 3D space and then attempting to normalize the pose of the simultaneously acquired 3D and 2D hand images.As a conclusion, contact-less hand biometrics is receiving an increasing attention in recent years, and many aspects remain unresolved such as invariant feature extraction or hand template creation.3.