Author:
Victor Chernyshov
Affiliation:
Lomonosov Moscow State Universitty, Russian Federation
Keyword(s):
Hand Biometrics, Client-server System, Continuous Skeletons, Hand Detection, Hand Shape, Finger Knuckle Print.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Mobile Imaging
Abstract:
In this paper, an efficient method for hand detection based on continuous skeletons approach is presented. It
showcased real-time working speed and high detection accuracy (3-5% both FAR and FRR) on a large dataset
(50 persons, 80 videos, 2322 frames). This makes the method suitable for use as a part of modern hand
biometrics systems including mobile ones. Next, the study shows that continuous skeletons approach can be
used as prior for object and background color models in segmentation methods with supervised learning (e.g.
interactive segmentation with seeds or abounding box). This fact was successfully adopted to the developed
client-server hand recognition system — both thumbnailed colored frame and extracted seeds are sent from
Android application to server where Grabcut segmentation is performed. As a result, more qualitative hand
shape features are extracted which is confirmed by several identification experiments. Finally, it is demonstrated
that hand detection results can be
used as a region of interest localization routine in the subsequent
analysis of finger knuckle print. The future research will be devoted to extracting features from dorsal fingers
surface and developing multi-modal classifier (hand shape and knuckle print features) for identification
problem.
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