Face recognition in static images and video sequences captured in unconstrained recording conditions is one of the most widely studied topics in computer vision due to its extensive applications in surveillance, law enforcement, bio-metrics, marketing, and so forth. Recently, methodologies that achieve good performance have been presented in top-tier computer vision conferences (e.g., ICCV, CVPR, ECCV etc.) and great progress has been achieved in face recognition with deep learning-based methods. Even though comprehensive benchmarks and extensive efforts exist for deep face recognition, very limited effort has been made towards benchmarking lightweight deep face recognition, which aims at model compactness and energy efficiency to enable efficient system deployment. In ICCV 2019, we make a significant step further and propose a new comprehensive benchmark, as well as organise the first challenge & workshop for lightweight deep face recognition.

Challenge Homepage: lightweight-face-recognition-challenge-workshop

Github Homepage: iccv19-lfr

8:30am ~ 12:30am 28th Oct 2019; Room 307BC

Rama Chellappa : Light Minimum-weight Face Recognition and Verification

Stan Z. Li : Techniques for Solving Challenging Problems in Face Recognition

Xiaoming Liu : On the Interpretability, Vulnerability, and Decomposability of Faces

Ji Lin : Design Automation for Efficient Deep Learning Computing

Link to Lightweight Face Recognition Challenge

Citation:

@inproceedings{deng2019lightweight,
                    title={Lightweight face recognition challenge},
                    author={Deng, Jiankang and Guo, Jia and Zhang, Debing and Deng, Yafeng and Lu, Xiangju and Shi, Song},
                    booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops},
                    pages={0--0},
                    year={2019}
                  }