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

• An AAM method with novel pal model is proposed for robust palm segmentation.
• The palm modeling is invariant from the posture of the hand and the fingers.
• There is no restriction in the background view of the hand images.
• Two metrics are developed to measure the performance of the palm model fitting.
• The results were achieved on a novel database constituted of 408 palm images.

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.