Pose-invariant 3D face recognition using half face

Liang, Yan; Zhang, Yun; Zeng, Xianxian

doi:10.1016/j.image.2017.05.004

Cited by 15 publications

(6 citation statements)

References 26 publications

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“…Later, they improved the registration method and proposed an across-pose method in [111]. [112] also proposed a 3D face recognition method with pose-invariant and a coarse-to-fine approach to detect landmarks under large yaw variations. At the coarse search step, HK curvature analysis is used to detect candidate landmarks and subdivide them according to the classification strategy based on facial geometry.…”

Section: B Local Feature-based Methodsmentioning

confidence: 99%

3D Face Recognition: A Survey

Jing,

Lu,

Gao

2021

Preprint

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Face recognition is one of the most studied research topics in the community. In recent years, the research on face recognition has shifted to using 3D facial surfaces, as more discriminating features can be represented by the 3D geometric information. This survey focuses on reviewing the 3D face recognition techniques developed in the past ten years which are generally categorized into conventional methods and deep learning methods. The categorized techniques are evaluated using detailed descriptions of the representative works. The advantages and disadvantages of the techniques are summarized in terms of accuracy, complexity and robustness to face variation (expression, pose and occlusions, etc). The main contribution of this survey is that it comprehensively covers both conventional methods and deep learning methods on 3D face recognition. In addition, a review of available 3D face databases is provided, along with the discussion of future research challenges and directions.

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Section: B Local Feature-based Methodsmentioning

confidence: 99%

3D Face Recognition: A Survey

Jing,

Lu,

Gao

2021

Preprint

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“…At that point, the mapping between the info signal and the yield result is acknowledged in the full association layer. Every convolution extricates the highlights of the information signal through a convolution activity of a convolution filter [20][21][22]. Examining layer is likewise called the "assembly" layer.…”

Section: Feature Extractionmentioning

confidence: 99%

Age-based facial recognition using convoluted neural network deep learning algorithm

Jien

Baharum

Wahab

et al. 2020

IJ-AI

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Face recognition is the use of biometric innovations that can see or validate a person by seeing and investigating designs depending on the shape of the individual. Face recognition is used largely for the purpose of well-being, despite the fact that passion for different areas of use is growing. Overall, face recognition innovations are worth considering because they have the potential for broad legal jurisdiction and different business applications. It is widely used in many spaces. How it works is a product of facial recognition processing facial geometry. The hole between the ear and the good way from the front to the jaw are the main variables. This code distinguishes the highlight of the face that is important for your facial separation and creates your facial expression. Therefore, this study gives an overview of age detection using a different combination of machine learning and image processing methods on the image dataset.

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“…Human reconstruction from images, especially for faces, is an important and challenging problem, which has drawn much attention from both academia and industry [15,24]. Although existing face reconstruction methods based on multiple images have achieved promising results, it is still a tough problem for a single input image, especially under partial occlusions and extreme poses.…”

Section: Introductionmentioning

confidence: 99%

Monocular 3D Face Reconstruction with Joint 2D and 3D Constraints

Cui

Yang

Lai

et al. 2022

Lecture Notes in Computer Science

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3D face reconstruction from a single image is a challenging problem, especially under partial occlusions and extreme poses. This is because the uncertainty of the estimated 2D landmarks will affect the quality of face reconstruction. In this paper, we propose a novel joint 2D and 3D optimization method to adaptively reconstruct 3D face shapes from a single image, which combines the depths of 3D landmarks to solve the uncertain detections of invisible landmarks. The strategy of our method involves two aspects: a coarse-to-fine pose estimation using both 2D and 3D landmarks, and an adaptive 2D and 3D re-weighting based on the refined pose parameters to recover accurate 3D faces. Experimental results on multiple datasets demonstrate that our method can generate high-quality reconstruction from a single color image and is robust for self-occlusions and large poses.

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Pose-invariant 3D face recognition using half face

Cited by 15 publications

References 26 publications

3D Face Recognition: A Survey

3D Face Recognition: A Survey

Age-based facial recognition using convoluted neural network deep learning algorithm

Monocular 3D Face Reconstruction with Joint 2D and 3D Constraints

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