1 research outputs found
Palmprint image registration using convolutional neural networks and Hough transform
Minutia-based palmprint recognition systems has got lots of interest in last
two decades. Due to the large number of minutiae in a palmprint, approximately
1000 minutiae, the matching process is time consuming which makes it
unpractical for real time applications. One way to address this issue is
aligning all palmprint images to a reference image and bringing them to a same
coordinate system. Bringing all palmprint images to a same coordinate system,
results in fewer computations during minutia matching. In this paper, using
convolutional neural network (CNN) and generalized Hough transform (GHT), we
propose a new method to register palmprint images accurately. This method,
finds the corresponding rotation and displacement (in both x and y direction)
between the palmprint and a reference image. Exact palmprint registration can
enhance the speed and the accuracy of matching process. Proposed method is
capable of distinguishing between left and right palmprint automatically which
helps to speed up the matching process. Furthermore, designed structure of CNN
in registration stage, gives us the segmented palmprint image from background
which is a pre-processing step for minutia extraction. The proposed
registration method followed by minutia-cylinder code (MCC) matching algorithm
has been evaluated on the THUPALMLAB database, and the results show the
superiority of our algorithm over most of the state-of-the-art algorithms.Comment: 6 figures, 8 page