CAN-GAN: Conditioned-attention normalized GAN for face age synthesis

Shi, Chenglong; Zhang, Jiachao; Yao, Yazhou; Sun, Yunlian; Rao, Huaming; Shu, Xiangbo

doi:10.1016/j.patrec.2020.08.021

Cited by 32 publications

(13 citation statements)

References 11 publications

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“…Shi et al [ 140 ] used GANs for face aging because different face parts have different ageing speeds over time. Hence, they used attention based conditional GAN using normalisation for handling the segmented face aging.…”

Section: Applicationsmentioning

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: Applicationsmentioning

confidence: 99%

3D Face Reconstruction in Deep Learning Era: A Survey

Sharma

Kumar

2022

Arch Computat Methods Eng

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“…The main problem associated with AdaIN operations is emphasised by Shi et al [49], who state that local agerelevant face regions are smoothed out caused by the equal normalisation of convolution feature maps. To address this issue, the authors proposed a Conditioned-Attention Normalised GAN framework.…”

Section: Referencementioning

confidence: 99%

“…• Motivated by the remarkable face image generation capabilities of StyleGAN [29], the lack of available child or elderly-based face images can be compensated by synthesizing ageing patterns of a single reference image via Style Transfer [91]. The works of Georgopoulos et al [48] and Shi et al [49] demonstrate the effectiveness of FAP based on (attention-based) instance normalization, thus inspiring further work in this direction.…”

Section: B Conceptual Challengesmentioning

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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“…Besides facial attributes translation, several GAN based methods have also been developed for other facial generative tasks. Age progression [11], [37]- [40] is the process of aesthetically rendering a given facial image to represent an ageing effect. Applying for makeup [13] transfer the makeup style from one facial image to the other one.…”

Section: Facial Synthesismentioning

confidence: 99%

Generative Adversarial Network for Multi Facial Attributes Translation

Liu

Feng

2021

IEEE Access

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Recently, Generative Adversarial Network (GAN) based approaches are applied in facial attribute translation. However, many tasks, i.e. multi facial attributes translation and background invariance, are not well handled in the literature. In this paper, we propose a novel GAN-based method that aims to get the target image that performs better within modifying one or more facial attributes in a single model. The model generator learns multi-points by inputting a re-coded transfer vector, ensuring the single model could learn multiple attributes simultaneously. It also optimizes the cycle loss to enhance the efficiency of transferring multi attributes. Moreover, the method uses the adaptive parameter to improve the calculation method of the loss function of the residual image. The results are also compared with the StarGAN v2, which is the current state-of-the-art model to prove the effectiveness and advancedness. Experiments show that our method has a satisfactory performance in multi facial attributes translation.

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CAN-GAN: Conditioned-attention normalized GAN for face age synthesis

Cited by 32 publications

References 11 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

Generative Adversarial Network for Multi Facial Attributes Translation

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