AAM-based palm segmentation in unrestricted backgrounds and various postures for palmprint recognition

In this paper, the AAM method with novel palm model is proposed for robust palm segmentation. The main advantages of this approach are the ability of efficient palm segmentation on the cluttered backgrounds and making a decision on whether the object in the scene is a palm with high accuracy. Especially, the proposed palm model eliminates the requirement that the whole hand image has to appear in the scene. The performance of the method is measured with two metrics which give more meaningful and quantitative results: the modified point-to-curve distance and a novel margin width suggested in this work. Furthermore, a novel device which performs the online palm image acquisition without any restriction has been developed. Experimental results on our palm image database denote that the proposed method is skillful for the palm segmentation and it can be used for further works. (C) 2013 Elsevier B.V. All rights reserved.