标题： 跨领域身份表示用于颅骨到面部匹配的基准数据集 显示英文标题

标题： Cross-Domain Identity Representation for Skull to Face Matching with Benchmark DataSet

摘要： 法医科学中的颅面重建对于识别犯罪和灾难的受害者至关重要。 目标是使用计算机视觉的最新进展，如深度学习，将给定的头骨映射到具有已知身份的人脸语料库中。 在本文中，我们提出了一种框架，用于根据头骨的X光图像识别一个人，使用卷积孪生网络进行跨域身份表示。 孪生网络是共享相同架构的两个网络，可以训练以发现一个特征空间，在该空间中，相似的观察结果被分组，而不相似的观察结果被分开。 为此，网络会接触到两组可比较和不同的数据。 然后最小化相似对之间的欧几里得距离，并最大化不相似对之间的欧几里得距离。 由于获取头骨和人脸图像对较为困难，我们准备了自己的包含40名志愿者的数据集，其中收集了他们的正面和侧面头骨X光图像以及光学人脸图像。 在收集的跨域数据集上进行了实验，以训练和验证孪生网络。 实验结果表明，从给定的头骨中识别一个人取得了令人满意的结果。 显示英文摘要

摘要： Craniofacial reconstruction in forensic science is crucial for the identification of the victims of crimes and disasters. The objective is to map a given skull to its corresponding face in a corpus of faces with known identities using recent advancements in computer vision, such as deep learning. In this paper, we presented a framework for the identification of a person given the X-ray image of a skull using convolutional Siamese networks for cross-domain identity representation. Siamese networks are twin networks that share the same architecture and can be trained to discover a feature space where nearby observations that are similar are grouped and dissimilar observations are moved apart. To do this, the network is exposed to two sets of comparable and different data. The Euclidean distance is then minimized between similar pairs and maximized between dissimilar ones. Since getting pairs of skull and face images are difficult, we prepared our own dataset of 40 volunteers whose front and side skull X-ray images and optical face images were collected. Experiments were conducted on the collected cross-domain dataset to train and validate the Siamese networks. The experimental results provide satisfactory results on the identification of a person from the given skull.