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The novel part-based cascaded regression algorithm research combining with pose estimation

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Abstract

Facial feature location is the key procedure in the process of face image analysis. To increase the localization accuracy and success rate of complex facial images due to lack of shape constraint in robust cascaded pose regression algorithm, the paper had proposed the novel part-based cascaded regression algorithm combining pose estimation. The facial landmarks are divided into several areas and pose estimation has used as shape constraint. To enhance the algorithm’s performance, a priori knowledge has been exerted during sampling shape-indexed features to constrain the distance between pixel’s pair, and the number of regression has reduced. The experimental results of proposed algorithm and others have demonstrated that the proposed algorithm has outperformed in localization accuracy and achieved better robustness for occlusion and complex facial images, the better localization success rate has been obtained, and the proposed method can realize real-time processing standard.

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China (61461017), Hainan Province Natural Science Foundation of Innovation Team Project (2017CXTD004), Innovative Research Project of Postgraduates in Hainan Province (Hyb2017-04).

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Authors and Affiliations

  1. State Key Laboratory of Marine Resources Utilization in South China Sea, Hainan University, Haikou, 570228, Hainan, China

    Qian Gao, Chong Shen & Kun Zhang

  2. College of Information Science and Technology, Hainan University, Haikou, 570228, Hainan, China

    Qian Gao, Chong Shen & Kun Zhang

  3. College of Ocean Information Engineering, Hainan Tropical Ocean University, Sanya, 572022, Hainan, China

    Kun Zhang

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  1. Qian Gao
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  2. Chong Shen
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  3. Kun Zhang
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Correspondence to Chong Shen.

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Gao, Q., Shen, C. & Zhang, K. The novel part-based cascaded regression algorithm research combining with pose estimation. Vis Comput 35, 1237–1244 (2019). https://doi.org/10.1007/s00371-018-1610-y

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