MobileFace: 3D Face Reconstruction with Efficient CNN Regression

Chinaev, Nikolai; Chigorin, Alexander; Laptev, Ivan

doi:10.1007/978-3-030-11018-5_3

Cited by 24 publications

(14 citation statements)

References 38 publications

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“…However, the joint loss function affects the quality of face shapes. Chinaev et al [ 75 ] developed a CNN based model for 3D face reconstruction using mobile devices. MobileFace CNN was used for the testing phase.…”

Section: D Face Reconstruction Techniquesmentioning

confidence: 99%

“…3DMM-CNN No RMSE Yes No [ 73 ] 2018 UV position maps Yes NME Yes No [ 74 ] 2018 Encoder-decoder network No RMSE Yes No [ 31 ] 2018 PIFR based 3DMM Yes Mean euclidean metric (MEM) No No [ 32 ] 2019 3DMM. Cascaded regression No RMSE and MAE No No [ 49 ] 2019 Blended model Yes Structural Similarity Index Metric (SSIM) No No [ 75 ] 2019 MobileNet CNN No Area Under the Curve (AUC) Yes No [ 58 ] 2019 Deep learning model Yes NME Yes Yes [ 27 ] 2019 3DMM fitting based on GANs and...…”

Section: D Face Reconstruction Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

3D Face Reconstruction in Deep Learning Era: A Survey

Sharma

Kumar

2022

Arch Computat Methods Eng

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3D face reconstruction is the most captivating topic in biometrics with the advent of deep learning and readily available graphical processing units. This paper explores the various aspects of 3D face reconstruction techniques. Five techniques have been discussed, namely, deep learning, epipolar geometry, one-shot learning, 3D morphable model, and shape from shading methods. This paper provides an in-depth analysis of 3D face reconstruction using deep learning techniques. The performance analysis of different face reconstruction techniques has been discussed in terms of software, hardware, pros and cons. The challenges and future scope of 3d face reconstruction techniques have also been discussed.

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Section: D Face Reconstruction Techniquesmentioning

confidence: 99%

Section: D Face Reconstruction Techniquesmentioning

confidence: 99%

3D Face Reconstruction in Deep Learning Era: A Survey

Sharma

Kumar

2022

Arch Computat Methods Eng

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“…e network consists of three parts: boundary heatmap estimator, boundary-aware landmarks regressor, and boundary effectiveness discriminator. e study in [30] estimates the 3D face shape with CNN by training the face image, and the face shape fits the corresponding 3D face model, which can detect the face feature and match the face contour. In addition, it solves the problem that different databases with different numbers of feature points cannot be cross-validated.…”

Section: Related Workmentioning

confidence: 99%

FRGAN: A Blind Face Restoration with Generative Adversarial Networks

Wei

Li²,

Chen³

et al. 2021

Mathematical Problems in Engineering

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Recent works based on deep learning and facial priors have performed well in superresolving severely degraded facial images. However, due to the limitation of illumination, pixels of the monitoring probe itself, focusing area, and human motion, the face image is usually blurred or even deformed. To address this problem, we properly propose Face Restoration Generative Adversarial Networks to improve the resolution and restore the details of the blurred face. They include the Head Pose Estimation Network, Postural Transformer Network, and Face Generative Adversarial Networks. In this paper, we employ the following: (i) Swish-B activation function that is used in Face Generative Adversarial Networks to accelerate the convergence speed of the cross-entropy cost function, (ii) a special prejudgment monitor that is added to improve the accuracy of the discriminant, and (iii) the modified Postural Transformer Network that is used with 3D face reconstruction network to correct faces at different expression pose angles. Our method improves the resolution of face image and performs well in image restoration. We demonstrate how our method can produce high-quality faces, and it is superior to the most advanced methods on the reconstruction task of blind faces for in-the-wild images; especially, our 8 × SR SSIM and PSNR are, respectively, 0.078 and 1.16 higher than FSRNet in AFLW.

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“…This posed a query if the morphable model fitting could be implemented by only using landmarks [25]. Texture based algorithms are computationally slow as compared to algorithms which are based on landmarks [26]. Liu et al [27] proposed the recovery of pose and expression normalized (PEN) 3D face shapes by using 2D face landmarks via cascaded regressors.…”

Section: Related Workmentioning

confidence: 99%

3D landmark‐based face restoration for recognition using variational autoencoder and triplet loss

Sharma

Kumar

2020

IET Biometrics

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Restoration of a 3D face from the mesh image is highly demanded in computer vision applications. 3D face restoration is a challenging task due to the variation of expression, poses, intrinsic geometries, and textures. The proposed technique consists of two main components, namely face restoration and recognition. A novel three‐dimensional (3D) landmark‐based face restoration method is proposed. 3D facial landmarks are used in the face recognition technique. It uses the principle of reflection and mid‐face plane for the restoration of facial landmarks. By using the restored 3D face, a deep learning‐based face recognition system is developed. It utilizes the concept of deep features from variational autoencoders. Further, these deep feature embeddings are trained using triplet loss training to increase the distance between embeddings of different persons and decreasing the distance between embeddings of the same person. These trained embeddings are used in support vector machine for prediction. The proposed framework is compared with recently developed face recognition techniques in terms of computational time. The proposed technique is able to recognize the person's face with better accuracy than the existing methods. Further, ablation studies are conducted to test the robustness of the proposed technique.

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MobileFace: 3D Face Reconstruction with Efficient CNN Regression

Cited by 24 publications

References 38 publications

3D Face Reconstruction in Deep Learning Era: A Survey

3D Face Reconstruction in Deep Learning Era: A Survey

FRGAN: A Blind Face Restoration with Generative Adversarial Networks

3D landmark‐based face restoration for recognition using variational autoencoder and triplet loss

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