用跨域特征匹配颅骨与人脸，提升刑侦身份识别准确率
Cross-Domain Identity Representation for Skull to Face Matching with Benchmark DataSet
- 构建孪生网络学习颅骨与人脸间的跨域身份特征
- 在40人自建数据集上实现高精度身份匹配
- 适合刑侦、法医领域应用，可推广至其他生物识别场景
法医颅面重建对刑事案件与灾难受害者的身份识别至关重要。本文提出一种基于卷积孪生网络的跨域身份表示框架，通过深度学习将给定的颅骨X光图像映射到已知身份的人脸图像库中。孪生网络由两个共享架构的子网络组成，训练目标是使相似样本（颅骨-人脸对）在特征空间中距离最小化，不相似样本距离最大化。由于真实颅骨与人脸配对数据稀缺，研究团队采集了40名志愿者的正面与侧面颅骨X光片及光学人脸图像，构建了首个公开的跨域匹配数据集。在该数据集上的实验验证了方法的有效性，实现了令人满意的个体识别性能。
原文摘要 · Abstract (English)
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.
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