10. Deep Learning for Tattoo Recognition

Soft biometrics are physiological and behavioral characteristics that provide some identifying information about an individual. Color of eye, gender, ethnicity, skin color, height, weight, hair color, scar, birthmarks, and tattoos are examples of soft biometrics. Several techniques have been proposed to identify or verify an individual based on soft biometrics in the literature. In particular, person identification and retrieval systems based on tattoos have gained a lot of interest in recent years. Tattoos, in some extent, indicate ones personal beliefs and characteristics. Hence, the analysis of tattoos can lead to a better understanding of ones background and membership to gang and hate groups. They have been used to assist law enforcement in investigations leading to the identification of criminals. In this chapter, we will provide an overview of recent advances in tattoo recognition and detection based on deep learning. In particular, we will present deep convolutional neural network-based methods for automatic matching of tattoo images based on the AlexNet and Siamese networks. Furthermore, we will show that rather than using a simple contrastive loss function, triplet loss function can significantly improve the performance of a tattoo matching system. Various experimental results on a recently introduced Tatt-C dataset will be presented.