BiTrackGAN：用于约束人脸老化的级联 CycleGAN
BiTrackGAN: Cascaded CycleGANs to Constraint Face Aging
AI总结：
针对门禁人脸数据库需随年龄更新的问题，提出级联两个 CycleGAN 的 BiTrackGAN 及自底向上训练约束机制，实现更合理、多样且准确的跨年龄人脸合成。
摘要：
随着现代计算机视觉技术准确率的提高，许多门禁系统都配备了人脸识别功能，以实现更快速的身份识别。为了保持较高的识别准确率，有必要使人脸数据库保持最新。然而，通过人工收集系统用户的最新面部照片是不切实际的。因此，我们针对所提出的网络提出了一种自底向上的训练方法，以应对这一挑战。本质上，我们提出的网络是一个翻译流水线，它将两个 CycleGAN 模块（一种被广泛使用的非配对图像到图像翻译生成对抗网络）级联起来，称为 BiTrackGAN。通过自底向上的训练，它在这两个 CycleGAN 模块之间引入了一个理想的中间状态，即约束机制。实验结果表明，与其他 CycleGAN 相关方法相比，BiTrackGAN 实现了更合理、更多样化的跨年龄面部合成。据我们所知，这是一种新颖且有效的约束机制，可通过 CycleGAN 方法实现更合理、更准确的老化合成。
英文摘要：
With the increased accuracy of modern computer vision technology, many access control systems are equipped with face recognition functions for faster identification. In order to maintain high recognition accuracy, it is necessary to keep the face database up-to-date. However, it is impractical to collect the latest facial picture of the system's user through human effort. Thus, we propose a bottom-up training method for our proposed network to address this challenge. Essentially, our proposed network is a translation pipeline that cascades two CycleGAN blocks (a widely used unpaired image-to-image translation generative adversarial network) called BiTrackGAN. By bottom-up training, it induces an ideal intermediate state between these two CycleGAN blocks, namely the constraint mechanism. Experimental results show that BiTrackGAN achieves more reasonable and diverse cross-age facial synthesis than other CycleGAN-related methods. As far as we know, it is a novel and effective constraint mechanism for more reason and accurate aging synthesis through the CycleGAN approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
