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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