2019 Looking at People Challenge CVPR

Challenge description

ChaLearn Challenges on Face Anti-Spoofing CVPR 2019

Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face Anti-spoofing benchmark datasets in recent years. However, existing face Anti-spoofing benchmarks have limited number of subjects(≤170) and modalities (≤2), which hinder the further development of the academic community.

To facilitate future face Anti-spoofing research, we release a large-scale multi-modal dataset, namely Chalearn CASIA-SURF, which is the largest publicly available dataset for face Anti-spoofing in terms of both subjects and visual modalities. Specifically, it consists of 1, 000 subjects with 21, 000 videos and each sample has 3 modalities (i.e., RGB, Depth and IR). An example image of the dataset samples is shown in the following.

More information can be found here and in our paper:

• Shifeng Zhang, Xiaobo Wang, Ajian Liu, Chenxu Zhao, Jun Wan, Sergio Escalera, Hailin Shi, Zezheng Wang, Stan Z. Li, " CASIA-SURF: A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing ", arXiv, 2018.

If you are interested in the CASIA-SURF dataset or the challenge in CVPR workshop 2019, please cite this paper.

Our challenge begins on Dec 22, 2018 ( codalab link), which will be hold on CVPR 2019 workshop. Winners and best papers will be awarded in cash (Baidu have confirmed to sponsor our CVPR 2019 workshop). We will organize a special issue in top journal (pending) and will also invite winners to submit their papers in it.

Prize