Tattoo Detection for Soft Biometric De-identification Based on Convolutional Neural Networks (CROSBI ID 638981)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Hrkać, Tomislav ; Brkić, Karla ; Kalafatić, Zoran
engleski
Tattoo Detection for Soft Biometric De-identification Based on Convolutional Neural Networks
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.
tattoo detection; convolutional neural networks; de-identification
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Podaci o prilogu
131-138.
2016.
objavljeno
Podaci o matičnoj publikaciji
1st OAGM-ARW Joint Workshop - Vision Meets Robotics
Kurt Niel, Peter M. Roth, Markus Vincze
OeAGM/AAPR
Podaci o skupu
1st OeAGM-ARW Joint Workshop
predavanje
11.05.2016-13.05.2016
Wels, Austrija