Robust local and global shape context for tattoo image matching

Abstract

Tattoos can provide useful information related to criminal gang activity. Law enforcement can use the information embedded in tattoos to identify and track the criminal history of a suspect. For matching processes, tattoo images are difficult to use due to problems such as deformations and weak edge structures. In this paper we describe a tattoo image retrieval and matching system based on a combination of local and global image matching methods to improve matching accuracy. The proposed local shape context combined with SIFT descriptors are used for local features of a tattoo object and global shape is used for overall shape of a tattoo object. The contributions of this paper include the introduction of a multiple different sizedbin polar histograms based local shape context (MHLC) and a global shape descriptor combining the multiple different sized-bin polar histogram and 2D Fourier Transform for robustness of translation, scale, rotation and shape distortions. We also describe robust similarity for local descriptors and a weighted matching method based on local and global descriptors. Our experimental results show that our proposed method performs better than previously published tattoo image retrieval systems.

Secure Remote Sensing Image Registration Based on Compressed Sensing in Cloud Setting

“…In 2010, Carmichael et al [4] proposed a similar global context descriptor for speeded up robust features (SURFs) and maximally stable extremal regions (MSERs). In 2015, Kim [5] proposed a local shape context combined with SIFT descriptors, which are used for local features of a tattoo object, and a global shape is used for the overall shape of a tattoo object. Ning et al [6] presented a feature matching algorithm based on a partial DAISY descriptor and global texture information.…”