Corpus ID: 231709671

Fast Facial Landmark Detection and Applications: A Survey

  title={Fast Facial Landmark Detection and Applications: A Survey},
  author={Kostiantyn Khabarlak and Larysa Koriashkina},
In this paper we survey and analyze modern neural-network-based facial landmark detection algorithms. We focus on approaches that have led to a significant increase in quality over the past few years on datasets with large pose and emotion variability, high levels of face occlusions – all of which are typical in real-world scenarios. We summarize the improvements into categories, provide quality comparison on difficult and modern in-the-wild datasets: 300-W, AFLW, WFLW, COFW. Additionally, we… Expand
Mobile Access Control System Based on RFID Tags And Facial Information
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300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge
The main goal of this challenge is to compare the performance of different methods on a new-collected dataset using the same evaluation protocol and the same mark-up and hence to develop the first standardized benchmark for facial landmark localization. Expand
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This work proposes three different approaches to generate manipulated faces in which two of them perform the manipulations via adversarial attacks and the other one uses known transformations to generate different manipulated faces using only one given face image. Expand
PFLD: A Practical Facial Landmark Detector
This paper investigates a neat model with promising detection accuracy under wild environments e.g., unconstrained pose, expression, lighting, and occlusion conditions) and super real-time speed on a mobile device. Expand
Style Aggregated Network for Facial Landmark Detection
This work proposes a style-aggregated approach to deal with the large intrinsic variance of image styles for facial landmark detection and demonstrates to perform well when compared with state-of-the-art algorithms on benchmark datasets AFLW and 300-W. Expand
Facial Landmark Detection: A Literature Survey
  • Yue Wu, Q. Ji
  • Computer Science
  • International Journal of Computer Vision
  • 2018
This paper performs an extensive review of the facial landmark detection algorithms and identifies future research directions, including combining methods in different categories to leverage their respective strengths to solve landmark detection “in-the-wild”. Expand
Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks
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Deep Facial Expression Recognition: A Survey
This survey provides a comprehensive review on deep FER, including datasets and algorithms that provide insights into overfitting caused by a lack of sufficient training data and expression-unrelated variations, such as illumination, head pose and identity bias. Expand
Robust Face Landmark Estimation under Occlusion
This work proposes a novel method, called Robust Cascaded Pose Regression (RCPR), which reduces exposure to outliers by detecting occlusions explicitly and using robust shape-indexed features, and shows that RCPR improves on previous landmark estimation methods on three popular face datasets. Expand
Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks
A new loss function, namely Wing loss, for robust facial landmark localisation with Convolutional Neural Networks (CNNs) is presented, and the superiority of the proposed method over the state-of-the-art approaches is proved. Expand
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