Face Alignment by Coarse-to-Fine Shape Estimation

WAN Jun; LI Jing; CHANG Jun; WU Yujia; XIAO Yafu; SONG Chengfang

doi:10.1049/cje.2018.09.014

Volume 27 Issue 6

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Article Navigation > Chinese Journal of Electronics > 2018 > 27(6): 1183-1191

WAN Jun, LI Jing, CHANG Jun, et al., “Face Alignment by Coarse-to-Fine Shape Estimation,” Chinese Journal of Electronics, vol. 27, no. 6, pp. 1183-1191, 2018, doi: 10.1049/cje.2018.09.014

Citation:

WAN Jun, LI Jing, CHANG Jun, et al., “Face Alignment by Coarse-to-Fine Shape Estimation,” Chinese Journal of Electronics, vol. 27, no. 6, pp. 1183-1191, 2018, doi: 10.1049/cje.2018.09.014

WAN Jun, LI Jing, CHANG Jun, et al., “Face Alignment by Coarse-to-Fine Shape Estimation,” Chinese Journal of Electronics, vol. 27, no. 6, pp. 1183-1191, 2018, doi: 10.1049/cje.2018.09.014

Citation:

WAN Jun, LI Jing, CHANG Jun, et al., “Face Alignment by Coarse-to-Fine Shape Estimation,” Chinese Journal of Electronics, vol. 27, no. 6, pp. 1183-1191, 2018, doi: 10.1049/cje.2018.09.014

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Face Alignment by Coarse-to-Fine Shape Estimation

doi: 10.1049/cje.2018.09.014

School of Computer Science, Wuhan University, Wuhan 430072, China

Funds: This work is supported by the National Natural Science Funds of China (No.41201404) and Fundamental Research Funds for the Central Universities of China (No.2042018gf0008).

More Information

Corresponding author: LI Jing (corresponding author) was born in 1967. He received the Ph.D. degree from Wuhan University, Wuhan, China, in 2006. He is currently a professor in Computer School of Wuhan University, Wuhan, China. His research interests include data mining and multimedia technology. (Email:leejingcn@163.com)
Received Date: 2017-07-10
Rev Recd Date: 2018-01-25
Publish Date: 2018-11-10

Abstract

Abstract

This paper presents a way to face alignment by Coarse-to-fine shape estimation (CFSE). Head poses, facial expressions and other facial appearance attributes are estimated coarsely as well as the main landmarks will be detected. The entire shape will be further estimated. This paper constructs an independent Head pose classification (HPC) model based on convolutional neural network to estimate and classify head poses. With the classification result, the estimated facial appearance attributes and the detected landmarks, a more accurate shape will be constructed. That shape will be used as the initialized shape and optimized by cascaded regression to approximate the ground-truth shape. Experiments on two challenging database demonstrate that CFSE outperforms the state-of-the-art methods.
- Facial appearance attributes,
- Head pose classification model,
- Convolutional neural network,
- Initialized shape,
- Cascaded regression

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References(25)

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