面向人脸特征点定位和姿态估计任务协同的DCNN方法

doi:10.3969/j.issn.1000-1158.2019.04.06

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Abstract In order to improve the performance of facial information recognition under complex conditions, a novel deep convolutional neural network (DCNN) method is proposed for the task coordination of facial landmark localization and head pose estimation.First, the facial information is detected from video images.Secondly, a DCNN model is designed for synergistic optimization of both facial landmark localization and head pose estimation tasks, and then used to simultaneously estimate the coordinates of facial landmarks and the angles of head pose,which are then fused to generate the human-computer interaction information.Finally, the proposed method is tested on both public datasets and actual scene data, and then compared with other state of the art methods.Experimental results show that the proposed approach performs better in facial landmark localization and pose estimation,and also achieves good accuracy and robustness in the HCI applications under complex conditions of illumination variations, expression changes and partial collusions, with the average speed at 16 frames per second, which demonstrates its efficiency and practicality.

Key words： metrology face recognition DCNN facial landmark pose estimation information fusion

Received: 26 June 2017 Published: 10 June 2019

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TB96

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	Articles by authors
	TIAN Zhuo
	SHE Qing-shan
	GAN Hai-tao
	MENG Ming

Cite this article:

TIAN Zhuo,SHE Qing-shan,GAN Hai-tao, et al. DCNN for Task Coordination of Facial Landmark Localization and Head Pose Estimation[J]. Acta Metrologica Sinica, 2019, 40(4): 576-582.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2019.04.06 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2019/V40/I4/576

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