Deep Structured Prediction for Facial Landmark Detection

• URL: http://arxiv.org/abs/2010.09035v1
• Date: Sun, 18 Oct 2020 17:09:24 GMT
• Title: Deep Structured Prediction for Facial Landmark Detection
• Authors: Lisha Chen, Hui Su, Qiang Ji
• Abstract summary: This paper proposes a method for deep structured facial landmark detection based on combining a deep Convolutional Network with a Conditional Random Field. We demonstrate its superior performance to existing state-of-the-art techniques in facial landmark detection.
• Score: 59.60946775628646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing deep learning based facial landmark detection methods have achieved excellent performance. These methods, however, do not explicitly embed the structural dependencies among landmark points. They hence cannot preserve the geometric relationships between landmark points or generalize well to challenging conditions or unseen data. This paper proposes a method for deep structured facial landmark detection based on combining a deep Convolutional Network with a Conditional Random Field. We demonstrate its superior performance to existing state-of-the-art techniques in facial landmark detection, especially a better generalization ability on challenging datasets that include large pose and occlusion.

Related papers

• DeepFidelity: Perceptual Forgery Fidelity Assessment for Deepfake Detection [67.3143177137102]
  Deepfake detection refers to detecting artificially generated or edited faces in images or videos. We propose a novel Deepfake detection framework named DeepFidelity to adaptively distinguish real and fake faces.
  arXiv  Detail & Related papers  (2023-12-07T07:19:45Z)
• COMICS: End-to-end Bi-grained Contrastive Learning for Multi-face Forgery Detection [56.7599217711363]
  Face forgery recognition methods can only process one face at a time. Most face forgery recognition methods can only process one face at a time. We propose COMICS, an end-to-end framework for multi-face forgery detection.
  arXiv  Detail & Related papers  (2023-08-03T03:37:13Z)
• RePFormer: Refinement Pyramid Transformer for Robust Facial Landmark Detection [131.1478251760399]
  We formulate the facial landmark detection task as refining landmark queries along pyramid memories. Specifically, a pyramid transformer head (PTH) is introduced to build both relations among landmarks and heterologous relations between landmarks and cross-scale contexts. A dynamic landmark refinement (DLR) module is designed to decompose the landmark regression into an end-to-end refinement procedure.
  arXiv  Detail & Related papers  (2022-07-08T14:12:26Z)
• Progressive Spatio-Temporal Bilinear Network with Monte Carlo Dropout for Landmark-based Facial Expression Recognition with Uncertainty Estimation [93.73198973454944]
  The performance of our method is evaluated on three widely used datasets. It is comparable to that of video-based state-of-the-art methods while it has much less complexity.
  arXiv  Detail & Related papers  (2021-06-08T13:40:30Z)
• Self-Guided Instance-Aware Network for Depth Completion and Enhancement [6.319531161477912]
  Existing methods directly interpolate the missing depth measurements based on pixel-wise image content and the corresponding neighboring depth values. We propose a novel self-guided instance-aware network (SG-IANet) that utilize self-guided mechanism to extract instance-level features that is needed for depth restoration.
  arXiv  Detail & Related papers  (2021-05-25T19:41:38Z)
• A region-based descriptor network for uniformly sampled keypoints [35.394659345406865]
  Matching keypoint pairs of different images is a basic task of computer vision. Most methods require customized extremum point schemes to obtain the coordinates of feature points with high confidence. In this paper, we design a region-based descriptor by combining the context features of a deep network.
  arXiv  Detail & Related papers  (2021-01-26T07:31:22Z)
• Robust Facial Landmark Detection by Multi-order Multi-constraint Deep Networks [35.19368350816032]
  We propose a Multi-order Multi-constraint Deep Network (MMDN) for more powerful feature correlations and shape constraints learning. An Implicit Multi-order Correlating Geometry-aware (IMCG) model is proposed to introduce the multi-order spatial correlations and multi-order channel correlations. An Explicit Probability-based Boundary-adaptive Regression (EPBR) method is developed to enhance the global shape constraints.
  arXiv  Detail & Related papers  (2020-12-09T09:11:47Z)
• Structured Landmark Detection via Topology-Adapting Deep Graph Learning [75.20602712947016]
  We present a new topology-adapting deep graph learning approach for accurate anatomical facial and medical landmark detection. The proposed method constructs graph signals leveraging both local image features and global shape features. Experiments are conducted on three public facial image datasets (WFLW, 300W, and COFW-68) as well as three real-world X-ray medical datasets (Cephalometric (public), Hand and Pelvis)
  arXiv  Detail & Related papers  (2020-04-17T11:55:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.