WebCaricature：漫画识别的基准
WebCaricature: a benchmark for caricature recognition
摘要：
研究漫画识别对于理解人脸感知具有重要意义。然而，在计算机视觉社区中进行的研究很少，主要是由于缺乏合适的数据集。本文建立了一个新的漫画数据集，旨在促进漫画识别的研究。所有的漫画和面部图像都是从网上收集的。与现有的两个数据集相比，这个数据集更具挑战性，有更多的可用图像，艺术风格和更大的个人内部变化。还提供了评估协议及其在数据集上的基线性能，以进行公平的比较。此外，本文还提出了一个漫画人脸识别框架，对漫画人脸识别面临的挑战进行了深入的分析。通过对挑战的分析，指出了值得进一步研究的问题。此外，基于评估协议和框架，提供了各种最先进的算法的基线性能。最后得出结论：该系统的性能还有很大的改进空间，所分析的问题还需要进一步研究。
英文摘要：
Studying caricature recognition is fundamentally important to understanding of face perception. However, little research has been conducted in the computer vision community, largely due to the shortage of suitable datasets. In this paper, a new caricature dataset is built, with the objective to facilitate research in caricature recognition. All the caricatures and face images were collected from the Web. Compared with two existing datasets, this dataset is much more challenging, with a much greater number of available images, artistic styles and larger intra-personal variations. Evaluation protocols are also offered together with their baseline performances on the dataset to allow fair comparisons. Besides, a framework for caricature face recognition is presented to make a thorough analyze of the challenges of caricature recognition. By analyzing the challenges, the goal is to show problems that worth to be further investigated. Additionally, based on the evaluation protocols and the framework, baseline performances of various state-of-the-art algorithms are provided. A conclusion is that there is still a large space for performance improvement and the analyzed problems still need further investigation.
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。
