Pamls Alignment Based On Two-Stage Convolutional Network with a Large in-Plane Rotation

Palms alignment is an important work for palmprint recognition in uncontrolled environment. Many methods have made progress to achieve alignment. But most of them ignore the palm's angles, which could not satisfy the alignment initialization when the hand has a large in-plane rotation. In this paper, we propose a palms alignment with affine transformation method based on a two-stage convolutional neural network (CNN). The basic idea is to rotate the target palm into the same angle category to avoid the following affine registration has a big matching error at the beginning. At the stage I, the given target palm is classified into two angle categories. At the stage II the upside down palm is firstly rotated 180 degrees, and then inputted into the subsequent feature extraction network, feature matching layer and regression network to achieve the affine alignment. Experimental results have proved the effectiveness of our method.