IEEE Trans Image Process. 2014 Dec;23(12):5654-69. doi: 10.1109/TIP.2014.2362658.
Face recognition algorithms are generally trained for matching high-resolution images and they perform well for similar resolution test data. However, the performance of such systems degrades when a low-resolution face image captured in unconstrained settings, such as videos from cameras in a surveillance scenario, are matched with high-resolution gallery images. The primary challenge, here, is to extract discriminating features from limited biometric content in low-resolution images and match it to information rich high-resolution face images. The problem of cross-resolution face matching is further alleviated when there is limited labeled positive data for training face recognition algorithms. In this paper, the problem of cross-resolution face matching is addressed where low-resolution images are matched with high-resolution gallery. A co-transfer learning framework is proposed, which is a cross-pollination of transfer learning and co-training paradigms and is applied for cross-resolution face matching. The transfer learning component transfers the knowledge that is learnt while matching high-resolution face images during training to match low-resolution probe images with high-resolution gallery during testing. On the other hand, co-training component facilitates this transfer of knowledge by assigning pseudolabels to unlabeled probe instances in the target domain. Amalgamation of these two paradigms in the proposed ensemble framework enhances the performance of cross-resolution face recognition. Experiments on multiple face databases show the efficacy of the proposed algorithm and compare with some existing algorithms and a commercial system. In addition, several high profile real-world cases have been used to demonstrate the usefulness of the proposed approach in addressing the tough challenges.
人脸识别算法通常是针对匹配高分辨率图像进行训练的，它们在类似分辨率的测试数据上表现良好。然而，当在不受约束的环境中（例如监控场景中的摄像机视频）捕获低分辨率人脸图像，并将其与高分辨率图库图像进行匹配时，此类系统的性能会下降。这里的主要挑战是从低分辨率图像中的有限生物特征内容中提取有区别的特征，并将其与信息丰富的高分辨率人脸图像匹配。当用于训练人脸识别算法的有标签正样本数据有限时，跨分辨率人脸匹配问题会进一步得到缓解。在本文中，我们解决了低分辨率图像与高分辨率图库匹配的跨分辨率人脸匹配问题。提出了一种协同迁移学习框架，它是迁移学习和协同训练范例的交叉授粉，用于跨分辨率人脸匹配。迁移学习部分将在训练过程中匹配高分辨率人脸图像时学到的知识转移到在测试过程中匹配低分辨率探测图像和高分辨率图库。另一方面，协同训练部分通过为目标域中的未标记探测实例分配伪标签来促进这种知识转移。这两个范例在提出的集成框架中的融合提高了跨分辨率人脸识别的性能。在多个人脸数据库上的实验表明了所提出算法的有效性，并与一些现有算法和商业系统进行了比较。此外，还使用了几个备受瞩目的真实案例来证明该方法在解决棘手挑战方面的有用性。
