A Novel Illumination-Insensitive Feature Extraction Method

Abstract

Image description is a great challenge in the field of computer vision under complex illumination condition. The complex illumination condition is usually unavoidable and unpredictable in real application. In this paper, we propose a novel generalized image descriptor, named as Anisotropy Weber Adapted Symmetric Ternary Pattern (AWASTP), which can overcome the directional inseparability of Weber Local Descriptor (WLD) and invariant threshold of Local Ternary Pattern (LTP). More particularly, we heighten the discriminative effectiveness of image description under complex illumination condition in several ways to restrain the effect of illumination variation. Firstly, a novel selection scheme for scale and angle is proposed. Based on this, an improved anisotropic Laplacian of Gaussian(ALOG) operator model is established by introducing the scale and angle parameter, moreover, an Anisotropic Weber Local Descriptor (AWLD) is presented, which can achieve more rich detailed information of illumination-insensitive feature. Secondly, an Adaptive Symmetric Ternary Pattern (ASTP) algorithm is proposed based on Weber criterion to generate more accurate threshold judgment according to the region characteristics. Thirdly, a twodimensional AWASTP histogram is created to enhance the discriminative power and represent illuminationinsensitive feature description. We conduct many experiments on benchmark databases, such as CMUPIE, FERET, PhoTex, RawFoot, and etc. Experimental results demonstrate the effectiveness of the proposed approach under different illumination conditions.

Finger Vein Recognition Based on a Histogram of Competitive Gabor Directional Binary Statistics

“…The extracted features not only have vein orientation characteristics but also reflect the degree of vein curvature. Tao et al (2020) proposed the discriminative local descriptor AWASTP, which constructs an anisotropic Laplacian Gaussian operator, and proposed an anisotropic Weber local descriptor, which can obtain richer light-insensitive features and detailed information to enhance identification. These methods can extract local discriminative structural information through the texture variation and orientation features in finger vein images, but the images captured through the device are susceptible to illumination, translation, noise, and rotation.…”