Weighted average pooling of deep features for tattoo identification

Miguel Nicolás-Díaz, Annette Morales-González, Heydi Méndez-Vázquez

Abstract: The use of deep learning in computer vision has been extremely successful. Nevertheless, for the tattoo recognition task very few approaches incorporate this technique, mainly due to the lack of large datasets to train specific models. Within this domain, some works have used the intermediate layers of pre-trained object classification networks to extract a global tattoo image descriptor, avoiding the expensive work of training from scratch. Although that approach showed good results, it does not incorporate specific knowledge of tattoo identification. In this work, we propose an attention pooling method that addresses this problem. Our method uses several functions to weight the local features of a convolutional feature map and then those weights are averaged using again some weights associated with each function. The use of these weighting functions provides more or less importance to local regions of the tattoo image, allowing the recognition process to take into account some domain-specific characteristics. This approach showed promising results in three tattoo databases, outperforming previous state-of-the-art works.