Tattoo detection based on CNN and remarks on the NIST database

Abstract

Detecting tattoo images stored in information technology (IT) devices of suspects is an important but challenging task for law enforcement agencies. Recently, the U.S. National Institute of Standards and Technology (NIST) held a challenge and released a tattoo database for the commercial and academic community in advancing research and development into automated image-based tattoo recognition technology. The best tattoo detection result in the NIST challenge was achieved by MorphoTrak with accuracy of 96.3%. This paper aims to answer three questions. 1) Is the NIST database suitable for training algorithms to detect tattoo images stored in IT devices of suspects? 2) Can convolutional neural networks (CNNs) outperform the MorphoTrak's algorithm? 3) How do training databases impact on tattoo detection performance? The NIST tattoo detection database containing 2,349 images and a database containing 10,000 collected from Flickr are utilized to answer these questions. The Flickr images taken in diverse environments and poses are used to simulate images stored in the IT devices. A CNN is trained on the NIST and Flickr images for this study. The experimental results demonstrate that the CNN outperforms the MorphoTrak's algorithm by 2.5%, achieving accuracy of 98.8% on the NIST database. When the CNN is trained on the NIST database to detect Flickr images, the accuracy drops to 65.8%. It implies that the NIST database is not an ideal database for training algorithms to detect tattoo images in IT devices of suspects. However, when the training database size increases, the detection performance improves.