@inproceedings{3d49be56315b427aa050c00ec64e4cfc,
title = "Hybrid Deep Convolutional Network for Face Alignment and Head Pose Estimation",
abstract = "Face alignment has been an important focus of vision research because it is the most fundamental step in face analysis, reconstruction, and applications of emotion and attention. However, face alignment still suffers from some problems, such as lack of stability and poor performance in practical applications due to occlusion, illumination, and high training costs. This paper proposes a Dual-Task Hybrid Deep Convolutional Network (DHDCN) to estimate head pose and facial landmark locations simultaneously. By connecting the multi-level features, the local features and global features can be effectively fused. Features common to both tasks are learned in the initial stages of the network, and later stages will train the two tasks independently. Although the results have some gaps compared to the state-of-the-art results, it also demonstrates the feasibility and potential of learning both tasks simultaneously.",
keywords = "Face alignment, Head pose, Multi-level feature",
author = "Zhiyong Wang and Jingjing Liu and Honghai Liu",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 15th International Conference on Intelligent Robotics and Applications, ICIRA 2022 ; Conference date: 01-08-2022 Through 03-08-2022",
year = "2022",
doi = "10.1007/978-3-031-13822-5\_46",
language = "英语",
isbn = "9783031138218",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "513--522",
editor = "Honghai Liu and Weihong Ren and Zhouping Yin and Lianqing Liu and Li Jiang and Guoying Gu and Xinyu Wu",
booktitle = "Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings",
address = "德国",
}