IEEE Trans Med Imaging. 2021 Mar;40(3):905-915. doi: 10.1109/TMI.2020.3041452. Epub 2021 Mar 2.
Forensic odontology is regarded as an important branch of forensics dealing with human identification based on dental identification. This paper proposes a novel method that uses deep convolution neural networks to assist in human identification by automatically and accurately matching 2-D panoramic dental X-ray images. Designed as a top-down architecture, the network incorporates an improved channel attention module and a learnable connected module to better extract features for matching. By integrating associated features among all channel maps, the channel attention module can selectively emphasize interdependent channel information, which contributes to more precise recognition results. The learnable connected module not only connects different layers in a feed-forward fashion but also searches the optimal connections for each connected layer, resulting in automatically and adaptively learning the connections among layers. Extensive experiments demonstrate that our method can achieve new state-of-the-art performance in human identification using dental images. Specifically, the method is tested on a dataset including 1,168 dental panoramic images of 503 different subjects, and its dental image recognition accuracy for human identification reaches 87.21% rank-1 accuracy and 95.34% rank-5 accuracy. Code has been released on Github. (https://github.com/cclaiyc/TIdentify).
法医学被认为是法医学的一个重要分支，它基于牙科鉴定来进行人类识别。本文提出了一种新的方法，使用深度卷积神经网络通过自动且准确地匹配二维全景牙科 X 射线图像来辅助人类识别。该网络设计为自顶向下的架构，包含一个改进的通道注意模块和一个可学习的连接模块，以更好地提取匹配特征。通过整合所有通道图之间的相关特征，通道注意模块可以选择性地强调相互依赖的通道信息，从而有助于获得更精确的识别结果。可学习的连接模块不仅以前馈的方式连接不同的层，而且还为每个连接层搜索最佳的连接，从而自动且自适应地学习层之间的连接。广泛的实验表明，我们的方法可以在使用牙科图像进行人类识别方面达到新的最先进的性能。具体来说，该方法在一个包含 503 个不同个体的 1168 张牙科全景图像的数据集上进行了测试，其牙科图像识别精度在人类识别方面达到了 87.21%的排名第一准确率和 95.34%的排名前五准确率。代码已在 Github 上发布（https://github.com/cclaiyc/TIdentify）。
