Peng Chunlei, Wang Nannan, Li Jie, Gao Xinbo
IEEE Trans Image Process. 2019 Apr 25. doi: 10.1109/TIP.2019.2912360.
Heterogeneous face recognition refers to matching facial images captured from different sensors or sources, which has wide applications in public security and law enforcement. Because of the great differences in sensing and creating procedure, there are huge feature gap between heterogeneous facial images. Existing methods merely focus on comparing the probe image with the gallery in feature space, while the true target may not appear at the first rank due to the appearance variations caused by different sensing patterns. In order to exploit valuable information from initial ranking result, this paper proposes to re-rank high-dimensional deep local representation for matching near-infrared (NIR) and visual (VIS) facial images, i.e. NIR-VIS face recognition. A high-dimensional deep local representation is firstly constructed by extracting and concatenating deep features on local facial patches via a convolutional neural network (CNN). The initial NIR-VIS recognition ranking results can be obtained by comparing the compressed deep features. We then propose a novel and efficient locally linear re-ranking (LLRe-Rank) technique to refine the initial ranking results, which can explore valuable information from initial ranking result. The proposed re-ranking method does not require any human interaction or data annotation, and can be served as an unsupervised post processing technique. Experimental results on the most challenging Oulu-CASIA NIR-VIS database and CASIA NIR-VIS 2.0 database demonstrate the effectiveness of our method.
异质人脸识别是指对从不同传感器或来源捕获的面部图像进行匹配，在公共安全和执法领域有广泛应用。由于传感和生成过程存在很大差异，异质面部图像之间存在巨大的特征差距。现有方法仅专注于在特征空间中将探测图像与图库进行比较，然而由于不同传感模式导致的外观变化，真实目标可能不会出现在首位。为了从初始排名结果中挖掘有价值的信息，本文提出对高维深度局部表示进行重新排序，以匹配近红外（NIR）和可见光（VIS）面部图像，即NIR-VIS人脸识别。首先通过卷积神经网络（CNN）在局部面部补丁上提取并拼接深度特征，构建高维深度局部表示。通过比较压缩后的深度特征可获得初始的NIR-VIS识别排名结果。然后，我们提出一种新颖且高效的局部线性重新排序（LLRe-Rank）技术来优化初始排名结果，该技术可以从初始排名结果中挖掘有价值的信息。所提出的重新排序方法不需要任何人工干预或数据标注，可作为一种无监督的后处理技术。在最具挑战性的奥卢-中科院自动化所NIR-VIS数据库和中科院自动化所NIR-VIS 2.0数据库上的实验结果证明了我们方法的有效性。
