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Sign-correlation cascaded regression for face alignment

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Abstract

Face alignment plays an important role in many applications such as face recognition and face reconstruction. Current regression based approaches can ease the multi-pose face alignment problem, but they fail to deal with the multiple local minima problem directly. To improve the performance of multi-pose facial landmark localization, in this paper we propose a sign correlation supervised descent method (SC-SDM) based on a nonlinear optimization theory. SC-SDM analyses the sign correlation between features and shapes and project both of them into a mutual sign-correlation subspace. By partitioning the whole multi-pose samples into a series of pose-consistent subsets, a group of models are learned from each subset. The experiments using the public multi-pose datasets has validated the partition and proved that SC-SDM can accurately separate samples into pose-consistent subsets, which reveals their latent relationships to pose. The comparison with state-of-the-art methods demonstrates that SC-SDM outperforms them, especially in uncontrolled conditions with various poses.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China

    Dansong Cheng, Yongqiang Zhang & Ce Liu

  2. School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin, 150001, China

    Xiaofang Liu

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  1. Dansong Cheng
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  2. Yongqiang Zhang
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  3. Ce Liu
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  4. Xiaofang Liu
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Correspondence to Xiaofang Liu.

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Cheng, D., Zhang, Y., Liu, C. et al. Sign-correlation cascaded regression for face alignment. Multimed Tools Appl 78, 26681–26699 (2019). https://doi.org/10.1007/s11042-019-7737-7

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