Deep Generic Features for Tattoo Identification

摘要

Recently, interest has grown in using tattoos as a biomet-ric feature for person identification. Previous works used handcrafted features for the tattoo identification task, such as SIFT. However, deep learning methods have shown better results than this kind of methods in many computer vision tasks. 'Faking into account that there are little research on tattoo identification using deep learning, we asses several publicly available CNNs models, pre-trained on large generic image databases, for the task of tattoo identification. We believe that, since tattoos mostly depict objects of the real world, their semantic and visual features might be related to those learned from a generic image database with real objects. Our experiments show that these models can outperform previous approaches without even fine-tuning them for tattoo identification. This allows developing tattoo identification applications with minimum implementation cost. Besides, due to the difficult access to public tattoo databases, we created two tattoo datasets and put one of them in public domain.