Recently, research communities on Computer Vision and biometrics have shown a lot of interest in face verification and classification methods. Fighting against Child Sexual Exploitation Material (CSEM) is one of the applications that might benefit most from these advances. In CSEM, discriminative parts of the face, i.e. mostly the eyes, are often occluded to make the victim identification more difficult. Most of the current face recognition methods are not able to handle such kind of occlusions. To overcome this problem, we present One-Shot Frequency Dominant Neighborhood Structure (OSF-DNS), a new perceptual hashing method that shows advantages on two scenarios: (a) occluded face verification, i.e., matching occluded faces with their non-occluded versions, and (b) face classification, i.e., getting the identity of an occluded face by means of a classifier trained with the non-occluded faces using the perceptual hash codes as feature vectors. We have compared the face verification performance of OSF-DNS with three perceptual hashing methods and with the features obtained from five deep learning techniques, using the occluded versions of six different facial datasets. The proposed method achieves accuracies between 69.24% and 99.46% depending on the dataset, and always higher than the compared methods. For the face classification task, we compared the performance of OSF-DNS with the features obtained by four deep learning techniques. Experimental results on LFW and CFPW datasets showed that the proposed hashing method outperformed the results obtained with deep features with an accuracy up to 89.53%.

最近，计算机视觉和生物识别的研究社区对人脸验证和分类方法表现出浓厚的兴趣。打击儿童性剥削材料 (CSEM) 是最可能从这些进步中受益的应用之一。在 CSEM 中，面部的辨别部分，即主要是眼睛，经常被遮挡，使受害者识别更加困难。目前大多数人脸识别方法都无法处理这种遮挡。为了克服这个问题，我们提出了 One-Shot Frequency Dominant Neighborhood Structure (OSF-DNS)，这是一种新的感知哈希方法，在两种情况下显示出优势：(a) 遮挡人脸验证，即将遮挡人脸与其非遮挡版本匹配, 和 (b) 人脸分类，即 通过使用感知哈希码作为特征向量对非遮挡人脸进行训练的分类器来获得被遮挡人脸的身份。我们使用六个不同面部数据集的遮挡版本，将 OSF-DNS 的面部验证性能与三种感知散列方法和从五种深度学习技术获得的特征进行了比较。根据数据集，所提出的方法的准确率在 69.24% 到 99.46% 之间，并且始终高于比较方法。对于人脸分类任务，我们将 OSF-DNS 的性能与四种深度学习技术获得的特征进行了比较。在 LFW 和 CFPW 数据集上的实验结果表明，所提出的哈希方法优于具有深度特征的结果，准确率高达 89.53%。