Deep learning architectures for tattoo detection and de-identification

Abstract

The widespread use of video recording devices to obtain recordings of people in various scenarios makes the problem of privacy protection increasingly important. Consequently, there is an increased interest in developing methods for de-identification, i.e. removing personally identifying features from publicly available or stored data. Most of related work focuses on de-identifying hard biometric identifiers such as faces. We address the problem of detection and de-identification of soft biometric identifiers - tattoos. We use a deep convolutional neural network to discriminate between tattoo and non-tattoo image patches, group the patches into blobs, and propose the de-identifying method based on replacing the color of pixels inside the tattoo blob area with a values obtained by interpolation of the surrounding skin color. Experimental evaluation on the contributed dataset indicates the proposed method can be useful in a soft biometric de-identification scenario.