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Deep Learning Architectures for Tattoo Detection and De- identification


Hrkać, Tomislav; Brkić, Karla; Ribarić, Slobodan; Marčetić, Darijan
Deep Learning Architectures for Tattoo Detection and De- identification // Sensing, Processing and Learning for Intelligent Machines (SPLINE), 2016 First International Workshop on
Aalborg, Denmark: IEEE, 2016. str. 45-49 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


Naslov
Deep Learning Architectures for Tattoo Detection and De- identification

Autori
Hrkać, Tomislav ; Brkić, Karla ; Ribarić, Slobodan ; Marčetić, Darijan

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

Izvornik
Sensing, Processing and Learning for Intelligent Machines (SPLINE), 2016 First International Workshop on / - Aalborg, Denmark : IEEE, 2016, 45-49

ISBN
978-1-4673-8917-4

Skup
First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE)

Mjesto i datum
Aalborg, Danska, 6.-8.7.2016

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
deep learning; tattoo detection; tattoo de-identification

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo



POVEZANOST RADA


Projekt / tema
HRZZ1544
HRZZ6733

Ustanove
Fakultet elektrotehnike i računarstva, Zagreb

Citiraj ovu publikaciju

Hrkać, Tomislav; Brkić, Karla; Ribarić, Slobodan; Marčetić, Darijan
Deep Learning Architectures for Tattoo Detection and De- identification // Sensing, Processing and Learning for Intelligent Machines (SPLINE), 2016 First International Workshop on
Aalborg, Denmark: IEEE, 2016. str. 45-49 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Hrkać, T., Brkić, K., Ribarić, S. & Marčetić, D. (2016) Deep Learning Architectures for Tattoo Detection and De- identification. U: Sensing, Processing and Learning for Intelligent Machines (SPLINE), 2016 First International Workshop on.
@article{article, year = {2016}, pages = {45-49}, keywords = {deep learning, tattoo detection, tattoo de-identification}, isbn = {978-1-4673-8917-4}, title = {Deep Learning Architectures for Tattoo Detection and De- identification}, keyword = {deep learning, tattoo detection, tattoo de-identification}, publisher = {IEEE}, publisherplace = {Aalborg, Danska} }