# Approach for Tattoo Detection and Identification Based on YOLOv5 and Similarity Distance
## 2 Related Works  
The literature analysis performed has shown that one of the first tattoo identification forensics was the keyword-based matching method. Law enforcement authorities followed the ANSI/NIST-ITL 1-2011 standard, which defines eight major classes (human, animal, plant, flag, object, abstract, symbol, and other) and a total of 70 subclasses (including male face, cat, narcotics, American flag, fire, figure, national symbols, and wording) to categorize tattoos [11] to assign a single keyword to the tattoo image in the database. However, as [PERSON] et al. [12] explain in their paper, in practice, searching for tattoo images based on keywords has several limitations: (1) the ANSI/NIST classes define a limited vocabulary that is not sufficient to describe different tattoo patterns; (2) several keywords may be required to accurately describe the tattoo image; (3) human annotation is subjective, meaning that different people can give quite different labels to the same tattoo. These deficiencies in the keyword-based tattoo image search system have led to the development of a Content-Based Image Recovery System (CBIR) to improve the efficiency and accuracy of tattoo search. To overcome the limitations of keyword-based tattoo matching, [PERSON] et al. [13] proposed the CBIR called Tattoo-ID to match tattoos using the image-to-image method. Tattoo-ID extracts key points from tattoo images with scale-invariant feature transform (SIFT) (Lowe) and uses an unsupervised ensemble ranking algorithm to measure visual similarities between two tattoos [14]. A brief review of the literature on tattoo detection and identification is presented in Table 1. Research, where the main aim was tattoo detection, was usually motivated by forensic applications aimed at building tattoo-content-based image search systems to help law enforcement. Therefore, [PERSON] and [PERSON] [15] proposed a system in which a cropped tattoo is segmented, represented by color, shape, and texture characteristics, and matched to the database. [PERSON] and [PERSON] [16] proposed an approach to the detection and segmentation of tattoo skin using image-negative methods in pre-processing to improve the retrieval and matching of tattoo images. The first step in this process was skin detection. The authors used various skin patches to perform the tasks of separating human skin color using the HSV model (hue, saturation, and value (or lightning)) model. In the second step, the negative image method was used to detect clear graphical images of the tattoo. In the third step, they extract the tattoo segment from the skin area of the negative image, and, as a result, negative images of the tattoos are obtained and can be used for further identification. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Authors** & **Object Detection/Localization Methods** & **Identification Methods** & **Dataset Name** & **Number of Tattoo Images** & **Non-Tatto Images** & **Results** \\ \hline [PERSON] et. al. [10] & Faster RCNN & n/a & \begin{tabular}{c} Tatt-C tuttoo vs. non-tattov \\ Flokr. into two vs. non-tattov \\ Flokr. into two vs. non-tattov \\ \end{tabular}  & \begin{tabular}{c} Tatt-C \\ 1349 \\ 5740 \\ \end{tabular}  & \begin{tabular}{c} Tatt-C \\ 1000 \\ 14200 \\ 4250 \\ \end{tabular}  & \begin{tabular}{c} Tatt-C \\ 98.25\%, Tat-C \\ 47540 \\ \end{tabular}  & \begin{tabular}{c} Nett-C \\ 1000 \\ 1000 \\ 1000 \\ 1000 \\ \end{tabular}  & \begin{tabular}{c} Nett-C \\ 1000 \\ 1000 \\ 1000 \\ 1000 \\ \end{tabular}  & \begin{tabular}{c} Nett-C \\ 98.25\%, close-dense neural network as classifier, with 10-fold cross-validation. \\ \end{tabular}  \\ \hline \ \begin{tabular}{c} [PERSON] and [PERSON] \\ [15] \\ \end{tabular}  &
\begin{tabular}{c} Deep-cropped tut  
The Bag-of-Words (BoW) model, which uses SIFT functions, was probably the most popular in the early CBIR system for tattoo search [19]. In addition to SIFT features, local binary patterns (LBP) and histograms of oriented gradients (HoG) features were also used in the research by [PERSON] et al.