Semi-Supervised FaceGAN for Face-Age Progression and Regression with Synthesized Paired Images

Pham, Quang Trung; Yang, Janghoon; Shin, Jitae

doi:10.3390/electronics9040603

Cited by 10 publications

(3 citation statements)

References 35 publications

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“…They used Markov Decision Process (MDP) for doing semantic manipulation. Pham et al [ 162 ] proposed a semi-supervised GAN technique to generate realistic face images. They synthesised the face images using the real data and the target age while training the network.…”

Section: Challenges and Future Research Directionsmentioning

confidence: 99%

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: Challenges and Future Research Directionsmentioning

confidence: 99%

3D Face Reconstruction in Deep Learning Era: A Survey

Sharma

Kumar

2022

Arch Computat Methods Eng

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“…Ning et al [92] 2020 Age group transitions CACD, private DB (Webcrawled) Or-El et al [25] 2020 Continuous ageing FFHQ-ageing Enables continuous ageing by interpolating between discrete age groups. Pham et al [93] 2020 Age group transitions UTKFace, FG-NET Sheng et al [94] 2020 Age group transitions CACD cGANs with rank-based discriminators [95]. Sun et al [96] 2020 Age group transitions MORPH-II Age labels are modeled as distributions instead of one-hot-encoded vectors.…”

Section: Referencementioning

confidence: 99%

Deep Face Age Progression: A Survey

2021

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Face Age Progression (FAP) refers to synthesizing face images while simulating ageing effects, thus enabling predicting the future appearance of an individual. The generation of age-progressed face images brings benefits for various applications, ranging from face recognition systems to forensic investigations and digital entertainment. In particular, the recent success achieved with deep generative networks significantly leveraged the quality of age-synthesized face images in terms of visual fidelity, ageing accuracy and identity preservation. However, the high number of contributions in recent years requires systematically structuring new findings and ideas to identify a common taxonomy, accelerate future research and reduce redundancy. Therefore, we present a comparative analysis of recent deep learning based face age progression methods for both adult and child-based face ageing, broken down into three high-level concepts: translation-based, condition-based, and sequence-based FAP. Further, we offer a comprehensive summary of the most common performance evaluation techniques, cross-age datasets, and open challenges to steer future research in the right direction.

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“…Quang T.M. Pham et al proposed a semi-supervised GAN (SS-FaceGAN) to deal with missing labeled datasets, which trains the model with synthesized paired images by two GAN models[27]. The main model called FaceGAN is based on the Conditional Adversarial Autoencoder (CAAE) model, but replacing the auto-encoder with a Unet architecture as the generator.…”

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confidence: 99%

Face progression and regression: a taxonomy and outlook

Huo

Wan²,

Zhuang³

2022

2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022)

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Age progression and regression is a task that aims at rendering face images with or without the "aging" effects. The problem is originally generated from the psychophysics and human perception community but now has found tremendous interests in the computer vision community in recent years. In this paper, we give a detailed analysis of the facial aging problem and conduct a comprehensive survey on the existing methods. There are many different methods available for face aging rendering, and each has its own advantages and purpose. We categorize the existing methods into three classes: physical-based models, example-based methods, and Deep learning-based methods. The first two classes are more traditional methods that have been developed in the last few decades, while the deep learning-based methods are leveraged on the huge success of the deep learning models that emerged in recent years. We review the representative works in each category and offer insights into future research on this topic.

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Semi-Supervised FaceGAN for Face-Age Progression and Regression with Synthesized Paired Images

Cited by 10 publications

References 35 publications

3D Face Reconstruction in Deep Learning Era: A Survey

3D Face Reconstruction in Deep Learning Era: A Survey

Deep Face Age Progression: A Survey

Face progression and regression: a taxonomy and outlook

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