## 2 Related Works  
The use of anti-counterfeiting systems based on unique features is increasing, with some research having been made regarding this topic. This section explores the current strategies used in those systems. According to [PERSON] [7], \"Fingerprinting an object is the ability to identify an individual object based on unique features such that if the object is seen by the system in other unfamiliar environments, or views, it can still be recognized\"; in other words, it is the individual object identification in different scenes based on unique features. The most common application, found in the literature, of extracting fingerprints from objects is a paper authenticity check in relevant documents. [PERSON] et al. [8] have found that the unique surface imperfections from paper, plastic cards, and product packaging contain an unique code formed by microscopic imperfections. These intrinsic characteristics can act as important features for the recognition system. The structure of some surfaces were examined using diffuse scattering of a collimated laser. To correlate the measurements obtained from the generated pattern of the laser speckle, cross-correlation was employed. [PERSON] et al. [9] devised PaperSpeckle, which takes advantage of the natural randomness present on paper to generate a fingerprint based on the texture speckle pattern. To capture the patterns, a mobile phone with a microscope attached to its camera, and a laptop/desktop connected to an USB microscope were used. For the extraction of the fingerprint from the speckle pattern, Gabor transform was employed. Beyond forgery detection/authenticity checks on paper, other applications of object fingerprinting include anti-counterfeiting systems for consumer safety, product traceability, among others. In 2014, [PERSON] and [PERSON] [10] proposed the Fingerprint Imaging by Binary Angular Reflection (FIBAR) method, which is an imaging method used to capture repeatable \"fingerprints\" from the surface of metal bolts. Since smooth metal surface does not provide enough features for individual object identification, the authors take advantage of a pear-skin finish to create a rough texture useful for identification purposes. The acquisition system is composed of a white diffuser made from translucent plastics, a black ring absorber and a macro lens. This setup prevents ambient light from falling on the metal surface, directly enabling repeatable image features to be captured from a common camera. To identify each bolt individually, the authors use Oriented FAST and Rotated BRIEF (ORB) [11] combined with a brute-force matcher with a cross-check. Moreover, the authors state that their approach can be applied to tiny mass-produced metal parts with the same finishing. The impact of this research may prevent metal bolt counterfeiting and/or help metal bolt traceability. This work was extended to an industrial setup in Reference [12]. In another work, \"A micro identifier dot on things\" (mIDoT) was proposed as an alternative to barcodes, tags, or marking for industrial parts traceability in Reference [13]. The dots were made using glitter or metallic-coloured ink, which is a mixture of basic ink with micro shiny particles that form random irregularities on the surface of the ink. The method used a modified version of FIBAR to capture micro surface irregularities as image features, enabling the possibility of individual object recognition. One thousand one hundred and seventy-two manually made ink dots on regular paper were uniquely identified achieving a perfect score on equal error rate. This work was extended [14] to an automatic dotting unit capable of dotting and capturing for numerous parts. The system comprises four different parts: (i) automatic dotting; (ii) image capturing; (iii) parts feeder unit; and (iv) image matching unit (PC). This system was tested in a database of 11,423 chip capacitors achieving an equal error rate of 0%. [PERSON] et al.