@inproceedings{c3f5fef6630c4534a46f270be94087d3,
title = "UIRNet: Facial Landmarks Detection Model with Symmetric Encoder-Decoder",
abstract = "One of the challenging problems for facial landmarks detection is learning important features from faces that contain different deformation of face shapes and pose. These important features include eye centres, jawline points, nose points, mouth corners etc that are helpful in various computer vision-related applications. The detection of facial landmarks is difficult when faces have a lot of variation in different conditions. These conditions could be various imaging conditions such as illumination, occlusion, or head poses. In this paper, we propose a deep learning-based facial landmarks detection model called Unet-Inception-ResNet (UIRNet) to predict distinct feature points. The model predicts 68-point landmarks from the detected faces from digital images or video.",
keywords = "convolutional neural network, encoder-decoder, facial keypoint detection",
author = "Savina Colaco and Yoon, {Young Jin} and Han, {Dong Seog}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 ; Conference date: 21-02-2022 Through 24-02-2022",
year = "2022",
doi = "10.1109/ICAIIC54071.2022.9722657",
language = "English",
series = "4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "407--410",
booktitle = "4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings",
address = "United States",
}