Abstract
In computer vision and pattern recognition, handcrafted local features play an important role in many tasks. Many effective handcrafted local features have been proposed. Among them, Weber Local Descriptor (WLD) is a successful one. WLD is a simple but powerful descriptor, and a lot of variants of WLD have also been proposed in recent years, which has been broadly used for texture classification as well as biometrics. In this paper, we make a review for WLD and its variants. Generally, the algorithms of WLD and its variants can be divided into categories such as differential excitation-based, orientation-based and multiple features based. We also summarize their applications for biometrics.
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