Shearing Invariant Texture Descriptor from a Local
Binary Pattern and its Application in Paper Fingerprinting
Omar
Wahdan, Mohammad Nasrudin, and Khairuddin Omar
Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Malaysia
Abstract: In this paper, a Shearing Invariant Texture Descriptor (SITD) is
proposed, which is a theoretically and computationally simple method based on
the Rotation invariant Local Binary Pattern (Rot-LBP) descriptor. In real-world
applications using flatbed scanners, such as paper texture fingerprinting, it’s
common for a sheet of paper to rotate during the image acquisition process.
Because the rotation is usually not based on the paper’s geometrical centre
pivot, the produced image is deformed with irregular rotation resulting in
shearing transforms. To tackle the shearing problem, the proposed SITD selects
a few patterns from the conventional Rot-LBP to achieve either horizontal or
vertical invariance. This paper presents the construction of the SITD operators
and their performance in recognizing self-developed and standard image
datasets, including real paper texture and Outex images, as well as those with
distinctive shapes. The images were distorted with only a shearing transform.
The self-developed images were distorted manually, while the standard images
were distorted by software. The proposed description method achieved up to 100%
correctly recognition rate in all the tested datasets based on the horizontal
shear invariant operator. In addition to the accurate performance in all the
conducted experiments, the operator significantly outperformed the Rot-LBP and
another benchmark method, the Shearing Moment Invariant (SMI). The superiority
of the descriptor in recognizing different types of patterns demonstrate its
ability to be used in applications where the shearing transform is present.
Keywords: Shear invariant descriptor, texture
fingerprint, image acquisition, local binary pattern, outex framework.
Received September 6, 2014; accepted February 4, 2015