The explosive growth of digital images in video surveillance and social media has led to the significant need for efficient search of persons of interest in law enforcement and forensic applications. Despite tremendous progress in primary biometric traits (e.g., face and fingerprint) based person identification, a single biometric trait alone can not meet the desired recognition accuracy in forensic scenarios. Tattoos, as one of the important soft biometric traits, have been found to be valuable for assisting in person identification. However, tattoo search in a large collection of unconstrained images remains a difficult problem, and existing tattoo search methods mainly focus on matching cropped tattoos, which is different from real application scenarios. To close the gap, we propose an efficient tattoo search approach that is able to learn tattoo detection and compact representation jointly in a single convolutional neural network (CNN) via multi-task learning. While the features in the backbone network are shared by both tattoo detection and compact representation learning, individual latent layers of each sub-network optimize the shared features toward the detection and feature learning tasks, respectively. We resolve the small batch size issue inside the joint tattoo detection and compact representation learning network via random image stitch and preceding feature buffering. We evaluate the proposed tattoo search system using multiple public-domain tattoo benchmarks, and a gallery set with about 300K distracter tattoo images compiled from these datasets and images from the Internet. In addition, we also introduce a tattoo sketch dataset containing 300 tattoos for sketch-based tattoo search. Experimental results show that the proposed approach has superior performance in tattoo detection and tattoo search at scale compared to several state-of-the-art tattoo retrieval algorithms.

中文翻译：

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大规模纹身图像搜索：联合检测和紧凑表示学习


视频监控和社交媒体中数字图像的爆炸式增长导致执法和取证应用中迫切需要有效搜索感兴趣的人员。尽管基于主要生物特征（例如面部和指纹）的人员识别取得了巨大进展，但仅凭单一生物特征无法满足法医场景中所需的识别精度。纹身作为重要的软生物特征之一，被发现对于协助身份识别很有价值。然而，在大量无约束图像中进行纹身搜索仍然是一个难题，现有的纹身搜索方法主要集中于匹配裁剪后的纹身，这与实际应用场景不同。为了缩小差距，我们提出了一种有效的纹身搜索方法，该方法能够通过多任务学习在单个卷积神经网络（CNN）中联合学习纹身检测和紧凑表示。虽然骨干网络中的特征由纹身检测和紧凑表示学习共享，但每个子网络的各个潜在层分别针对检测和特征学习任务优化共享特征。我们通过随机图像拼接和前置特征缓冲解决了联合纹身检测和紧凑表示学习网络内的小批量问题。我们使用多个公共域纹身基准以及一个包含约 30 万张分散注意力的纹身图像的图库来评估所提出的纹身搜索系统，这些图像是根据这些数据集和来自互联网的图像编译的。此外，我们还引入了包含 300 个纹身的纹身素描数据集，用于基于素描的纹身搜索。 实验结果表明，与几种最先进的纹身检索算法相比，所提出的方法在大规模纹身检测和纹身搜索方面具有优越的性能。