Abstract
In this paper, we propose a new face landmark detection method. In previous methods, face detection was essential before a face landmark detection. The disadvantage of these methods is that they are greatly affected by the performance of the face detection model. In order to overcome this disadvantage, we proposed a method to simultaneously detect the face region and the face landmark. The basic idea came from 1-stage object detection. The structure of the Yolo v3 model, a representative 1-stage object detection model, was modified to find the landmark, and the loss function for training was modified to learn the coordinates of the landmark. In addition, MobileNet was used as the backbone network to increase the processing speed. In order to check the performance of the proposed model, the model was trained using the 300 W-LP database. It was then tested using Helen and LFPW databases, and the average normalized error was used as the evaluation metric. As a result of the evaluation, it was confirmed that the proposed model has improved performance over the previous methods.
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Acknowledgement
This work was supported by the NRF(National Research Foundation) of Korea funded by the Korea government (Ministry of Science and ICT) (NRF-2019R1A2C4070681).
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Kim, T., Mok, J.W., Lee, E.C. (2021). 1-Stage Face Landmark Detection Using Deep Learning. In: Singh, M., Kang, DK., Lee, JH., Tiwary, U.S., Singh, D., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2020. Lecture Notes in Computer Science(), vol 12616. Springer, Cham. https://doi.org/10.1007/978-3-030-68452-5_25
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