Physical object identification based on FAMOS microstructure fingerprinting: Comparison of templates versus invariant features

• Published in: 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA) pp. 119 - 123
• Main Authors: Diephuis, Maurits, Voloshynovskiy, Sviatoslav
• Format: Conference Proceeding
• Language: English
• Published: University of Trieste and University of Zagreb 01.09.2013
• Subjects: Cameras; Feature extraction; Indexes; Microstructure; Probes; Synchronization
• ISSN: 1845-5921
• DOI: 10.1109/ISPA.2013.6703725

In this paper, we address the problem of physical object identification based on optical non-cloneable surface microstructure images. Physical object identification is an emerging problem raised in mobile multimedia applications that interact with physical objects as well as in physical world security applications for which there is a great need for reliable, fast and secure object verification. One of the most crucial problems in the design of identification systems is optimal feature selection and extraction which are characterised by their high distinguishability and robustness to lightening variations and geometrical transforms. Not less an important aspect of feature selection is their vulnerability to counterfeiting or physical cloning that we refer to as physical security. Since the geometric de-synchronization represents one of the most significant challenges in the design of reliable physical object identification/authentication systems, we will investigate this problem using two techniques that are well established in computer vision applications and compare the performance of both systems. In particular, we consider two different strategies based on special graphical marks present on physical objects such as packaging or watches which can be considered as templates and microstructure features extracted based on the popular SIFT descriptors. To evaluate the performance of both approaches we use the FAMOS database which contains 5000 unique carton packages acquired 6 times each with two different cameras. The performance of the systems is evaluated based on the empirically ascertained probabilities of miss and false acceptance.