Tattoos are still poorly explored as a biometrics factor for human identification, especially in public security, where tattoos can play an important role for identifying criminals and victims. Tattoos are considered a soft biometrics, since they are not permanent and can change along time, differently from hard biometrics traits (fingerprint, iris, DNA, etc). The identification of tattoos are not simple, since they do not have a definite pattern or location. This fact increases the complexity of developing models to address this problem. In addition, the tattoo identification roadmap is very complex, including several steps and, in each step, specific methods need to be developed. Among the several problems identified in this roadmap, we tacked the identification problem, which is defined as: given an image of a person, determine if there is a tattoo or not. We present a deep learning model based on transfer learning for the tattoo detection problem. We also used data augmentation to improve the diversity of the training sets so as to achieve better classification accuracy. Along the work two new datasets for tattoo detection were created. Several comparative experiments were done to evaluate the diversity of images in the datasets, and the accuracy of the proposed model. Results were very promising, achieving an accuracy of 95.1% in the test set, and a F1-score of 0.79 in an external dataset. Overall, results were satisfactory, given the complexity of the problem. Future work will focus on expanding the datasets created and addressing the other problems of the tattoo roadmap.