Landmark detection
In
Applications
Facial landmarks
Finding facial landmarks is an important step in facial identification of people in an image. Facial landmarks can also be used to extract information about mood and intention of the person.
Holistic methods are pre-progammed with statistical information on face shape and landmark location coefficients. The classic holistic method is the
Medical images
Cephalometry
Fashion
The purpose of landmark detection in fashion images is for classification purposes. This aids in the retrieval of images with specified features from a database or general search. An example of a fashion landmark is the location of the hemline of a dress. Fashion landmark detection is particularly difficult due to the extreme deformation that can occur in clothing.[9]
Some classical methods of feature detection such as scale-invariant feature transform have been used in the past. However, it is now more common to use deep learning methods. This has been helped along enormously by the publication of a number of large fashion datasets that can be used for training.[10] These methods include regression-based models, constraint-based models, and attentive models.[11] The particular problems of fashion landmark detection (deformation) have led to pose estimation models which detect and take into account the pose of the model wearing the clothes.[12]
Methods
There are several
Deep Learning
Deep learning has had a significant impact on autonomous facial landmark detection by enabling more accurate and efficient detection of landmarks in real-world photos.
In particular, solutions based on this approach have achieved
Evolutionary algorithm
Evolutionary algorithms at the training stage try to learn the method of correct determination of landmarks. This phase is an iterative process and, accordingly, is performed in several iterations. As a result of the completion of the last iteration, a system will be obtained that can correctly determine the landmark with a certain accuracy. In the particle swarm optimization method, there are particles that search for landmarks, and each of them uses a certain formula in each iteration to optimize landmark detection.[15]
References
- ^ Wu & Ji, p. 115.
- ^ Wu & Ji, p. 116.
- ^ Wu & Ji, p. 116.
- ^ Wu & Ji, p. 117.
- ^ Wu & Ji, p. 118.
- ^ Wu & Ji, p. 118.
- ^ Wu & Ji, p. 119.
- ^ Wu & Ji, p. 119.
- ^ Zhang, Zhang & Du, p. 1.
- ^ Zhang, Zhang & Du, p. 1.
- ^ Zhang, Zhang & Du, pp. 1–4.
- ^ Zhang, Zhang & Du, p. 2.
- ^ Wu & Ji
- arXiv:1907.06724 [cs.CV].
- CiteSeerX 10.1.1.72.3218.
Bibliography
- Schwendicke, Falk; Chaurasia, Akhilanand; Arsiwala, Lubaina; Lee, Jae-Hong; Elhennawy, Karim; Jost-Brinkmann, Paul-Georg; Demarco, Flavio; Krois, Joachim (2021). "Deep learning for cephalometric landmark detection: Systematic review and meta-analysis". Clinical Oral Investigations. 25 (7): 4299–4309. S2CID 235232149.
- Wu, Yue; Ji, Qiang (2019). "Facial Landmark Detection: A Literature Survey". International Journal of Computer Vision. 127 (2): 115–142. S2CID 255101562.
- Zhang, Yungang; Zhang, Cai; Du, Fei (2019). "A Brief Review of Recent Progress in Fashion Landmark Detection". 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). pp. 1–6. S2CID 210931275.