Cascade learning from adversarial synthetic images for accurate pupil detection

作者：

Highlights：

• We propose a unified framework to learn shape augmented cascade regression models for accurate pupil detection.

• We exploit the adversarial training to refine the synthetic eyes with texture and appearance from real images.

• By leveraging the power of cascade regression, the proposed method iteratively estimate the eye related key point locations.

• The proposed framework achieves the state-of-the-art results of pupil detection on BioID, GI4E and LFW.

摘要

•We propose a unified framework to learn shape augmented cascade regression models for accurate pupil detection.•We exploit the adversarial training to refine the synthetic eyes with texture and appearance from real images.•By leveraging the power of cascade regression, the proposed method iteratively estimate the eye related key point locations.•The proposed framework achieves the state-of-the-art results of pupil detection on BioID, GI4E and LFW.

论文关键词：Cascade regression,GANs,Pupil detection

论文评审过程：Received 24 July 2018, Revised 19 November 2018, Accepted 15 December 2018, Available online 18 December 2018, Version of Record 21 December 2018.

论文官网地址：https://doi.org/10.1016/j.patcog.2018.12.014