Yang Xi, Guo Haoyuan, Wang Nannan, Song Bin, Gao Xinbo
IEEE Trans Image Process. 2020 Apr 1. doi: 10.1109/TIP.2020.2983554.
Security inspection aims to improve the high detection rate as well as reduce the false alarm rate. However, it still suffers from two challenges affecting its robustness. 1) Existing security inspection methods are mostly designed for natural images, which cannot reflect the uniqueness and imaging principle of THz images. 2) Existing methods is sensitive to noise interference and pose variations. This work revisits these challenges and presents a novel symmetry driven Siamese network (SDSN) for THz concealed object verification. Our idea is to employ a specially designed network architecture for THz concealed object verification. First, to reflect the uniqueness and the special property of THz images, Siamese network with Contrastive loss is used for feature extraction along with symmetrical prior information consideration, which can learn symmetrical metrics from the same person. Second, to alleviate the impact of noise interference and pose variations, the adaptive identity normalization (A-IDN) is proposed to normalize the symmetrical metrics each person. Finally, to enhance the generalization of network, an adaptive selective threshold based on Gaussian mixture model (AST-GMM) is designed, which serves as a classifier for the final classification results. Extensive experiments show that SDSN significantly improves the accuracy. Specially, SDSN outperforms the state-of-the-art methods without symmetrical prior information on THz security dataset.
安全检查旨在提高检测率并降低误报率。然而，它仍然面临两个影响其鲁棒性的挑战。1) 现有的安全检查方法大多是针对自然图像设计的，无法反映太赫兹图像的独特性和成像原理。2) 现有方法对噪声干扰和姿态变化敏感。这项工作重新审视了这些挑战，并提出了一种用于太赫兹隐藏物体验证的新型对称驱动暹罗网络（SDSN）。我们的想法是采用一种专门设计的网络架构来进行太赫兹隐藏物体验证。首先，为了反映太赫兹图像的独特性和特殊属性，带有对比损失的暹罗网络用于特征提取，并考虑对称先验信息，这可以从同一个人身上学习对称度量。其次，为了减轻噪声干扰和姿态变化的影响，提出了自适应身份归一化（A-IDN）来对每个人的对称度量进行归一化。最后，为了增强网络的泛化能力，设计了一种基于高斯混合模型的自适应选择阈值（AST-GMM），它作为最终分类结果的分类器。大量实验表明，SDSN显著提高了准确率。特别是，在太赫兹安全数据集上，SDSN优于没有对称先验信息的现有最先进方法。
