Tattoo Recognition Based on Triplet GAN

An important feature of the deep learning algorithm is that the hidden layer of the neural network is more dependent on more computing resources and a larger amount of data. In this paper, we use TripletGAN method to identify human body images with tattoos using fewer data sample labels. The model achieved better results(0.9462) than the only Triplet(0.8352) and only GAN(0.9178) in the MNIST dataset, it also achieved higher recognition accuracy (77-82%) under the premise of saving the amount of data, compared with the traditional machine learning algorithm(54-84%). We provide a new idea for tattoo image recognition applications in embedded computing units.