Naslov
Tattoo Detection for Soft Biometric De-identification Based on Convolutional Neural Networks

Autori
Hrkać, Tomislav ; Brkić, Karla ; Kalafatić, Zoran

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
1st OAGM-ARW Joint Workshop - Vision Meets Robotics / Kurt Niel, Peter M. Roth, Markus Vincze - : OeAGM/AAPR, 2016, 131-138

Skup
1st OeAGM-ARW Joint Workshop

Mjesto i datum
Wels, Austrija, 11.05.2016. - 13.05.2016

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
tattoo detection; convolutional neural networks; de-identification

Sažetak
Nowadays, video surveillance is ubiquitous, posing a potential privacy risk to law-abiding individu- als. Consequently, there is an increased interest in developing methods for de-identification, i.e. re- moving personally identifying features from publicly available or stored data. While most of related work focuses on de-identifying hard biometric identifiers such as faces, we address the problem of de-identification of soft biometric identifiers – tattoos. We propose a method for tattoo detection in unconstrained images, intended to serve as a first step for soft biometric de-identification. The method, based on a deep convolutional neural network, discriminates between tattoo and non- tattoo image patches, and it can be used to produce a mask of tattoo candidate regions. We contribute a dataset of manually labeled tattoos. Experimental evaluation on the contributed dataset indicates competitive performance of our method and proves its usefulness in a de-identification scenario.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA