## 1 Introduction  
In the context of anti-counterfeiting systems, a new concept named Recognition of Individual Objects using Tagless Approaches (RIDTA), along with an approach to prevent wine counterfeiting have been introduced [1]. In that work, the anti-counterfeiting scheme is achieved in a two phase process: the enrollment phase, and the verification phase. In the enrollment phase, during the bottling process, every wine bottle is registered in a database by capturing a photo of the top surface of the cork stopper. In the verification phase a common user, using a camera (e.g., smartphone camera) takes a photo of the same region of interest (the enrolled region), which is uploaded to a server, where a computer vision application capable of discerning if the query image has a match in the database is applied. Finally, the relevant information is sen back to the user, notifying him if the bottle had previously been registered or not, indicating if the wine bottle is genuine or not. The high precision and recall rates presented come from the success of capturing repeatable features from the top surface of the cork stoppers, necessary for the computer vision recognition system. To capture these repeatable features across different camera sensors, a patented technology [2] was devised. This technology comprises an illumination device and the computer vision method. In this work, the illumination device is presented, tested, and discussed. It includes an illuminator ring arranged for illuminating tangentially with grazing light towards the centre of said ring, and an optical lens allowing focus at a short distance. The effectiveness of the illumination device in capturing high-contrast stable photos with repeatable cork features is also demonstrated. Moreover, the computer vision recognition system is extensively tested on its generalization capabilities. Four different databases were utilized: the improved private cork database, comprising 1500 photos from 500 cork stoppers; and, in order to compare with other researcher's works, three public iris databases were employed, namely the CASIA-IrisV1 database (CASIA-IrisV1, [[http://biometrics.idealtest.org/](http://biometrics.idealtest.org/)]([http://biometrics.idealtest.org/](http://biometrics.idealtest.org/))), the MMUI iris database [3] and the IIT Delhi iris database [4]. The results clearly show the success of capturing and extracting repeatable cork surface features using the proposed illumination device across three different smartphone rear cameras. Regarding the computer vision recognition system that suits the RIOTA method, a significant improvement of the results from Reference [1] is shown. RIOTA's implementation achieved a perfect score in False Acceptance Rate, False Rejection Rate, accuracy and Equal Error Rate (EER) on the private database. In respect to the publicly available iris databases, the recognition results demonstrate that the RIOTA method is able to compete with the state of art methods specially designed for iris recognition. Finally, this work advocates the cork as a Unique Object, part of the Physical Unclonability and Disorder system. PUD security systems take advantage of the randomness and disorder of some physical features of a given object to uniquely identify it. Some advantages of these kind of systems are [5]:  
* Avoiding storing secret digital keys on vulnerable hardware;
* Natural feature disorder is very hard to clone and falsify. As a consequence, it becomes very expensive to replicate these kind of features;
* The existence of this randomness can be a valuable asset, allowing the use of some cryptographic protocols. PUD systems are divided in two major types: Unique Objects (UNOs) and Physical Unclonable Functions (PUFs). Unique Objects are defined as physical systems displaying a set of unique and hard to clone features (i.e., a human \"fingerprint\"), which can be measured using external equipment. Ideally, this equipment would have to be cost-effective and enable fast calculation of the unique features.