Learning Face Age Progression: A Pyramid Architecture of GANs

Yang, Hongyu; Huang, Di; Wang, Yunlong; Jain, Anil K.

doi:10.1109/cvpr.2018.00011

Cited by 188 publications

(226 citation statements)

References 34 publications

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“…Recently, advances in Generative Adversarial Networks (GANs) [16] have made tremendous progress in synthesizing realistic faces [1,29,25,12], like face aging [46], pose changing [44,21] and attribute modifying [4]. However, these existing approaches still suffer from some quality issues, like lack of fine details in skin, difficulty in dealing with hair and background blurring.…”

Section: Introductionmentioning

confidence: 99%

Mask-Guided Portrait Editing With Conditional GANs

Bao

Yang

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

143

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a) Mask2image (b) Component editing (c) Component transfer Figure 1: We propose a framework based on conditional GANs for mask-guided portrait editing. (a) Our framework can generate diverse and realistic faces using one input target mask (lower left corner in the first image). (b) Our framework allows us to edit the mask to change the shape of face components, i.e. mouth, eyes, hair. (c) Our framework also allows us to transfer the appearance of each component for a portrait, including hair color. AbstractPortrait editing is a popular subject in photo manipulation. The Generative Adversarial Network (GAN) advances the generating of realistic faces and allows more face editing. In this paper, we argue about three issues in existing techniques: diversity, quality, and controllability for portrait synthesis and editing. To address these issues, we propose a novel end-to-end learning framework that leverages conditional GANs guided by provided face masks for generating faces. The framework learns feature embeddings for every face component (e.g., mouth, hair, eye), separately, contributing to better correspondences for image translation, and local face editing. With the mask, our network is available to many applications, like face synthesis driven by mask, face Swap+ (including hair in swapping), and local manipulation. It can also boost the performance of face parsing a bit as an option of data augmentation.

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Section: Introductionmentioning

confidence: 99%

Mask-Guided Portrait Editing With Conditional GANs

Bao

Yang

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

143

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“…1) A novel GAN based method for age progression, which incorporates face verification and age estimation techniques, thereby addressing the issues of aging effect generation and identity cue preservation in a coupled manner; 2) A pyramid-structured discriminator for GAN-based face synthesis, which well simulates both global muscle sagging and local subtle wrinkles; 3) An adversarial learning scheme to simultaneously train a single generator and multiple parallel discriminators, which is able to generate smooth continuous aging sequences even if only faces from discrete age clusters are provided; 4) New validation experiments in addition to existing protocols, including COTS face recognition system based evaluation and robustness assessment to the changes in expression, pose, and makeup. A preliminary version of this paper was published in [18]. This paper significantly improves [18] in the following aspects.…”

Section: Introductionmentioning

confidence: 94%

Learning Continuous Face Age Progression: A Pyramid of GANs

Yang

Huang

Wang

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

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The two underlying requirements of face age progression, i.e. aging accuracy and identity permanence, are not well studied in the literature. This paper presents a novel generative adversarial network based approach to address the issues in a coupled manner. It separately models the constraints for the intrinsic subject-specific characteristics and the age-specific facial changes with respect to the elapsed time, ensuring that the generated faces present desired aging effects while simultaneously keeping personalized properties stable. To ensure photo-realistic facial details, high-level age-specific features conveyed by the synthesized face are estimated by a pyramidal adversarial discriminator at multiple scales, which simulates the aging effects with finer details. Further, an adversarial learning scheme is introduced to simultaneously train a single generator and multiple parallel discriminators, resulting in smooth continuous face aging sequences. The proposed method is applicable even in the presence of variations in pose, expression, makeup, etc., achieving remarkably vivid aging effects. Quantitative evaluations by a COTS face recognition system demonstrate that the target age distributions are accurately recovered, and 99.88% and 99.98% age progressed faces can be correctly verified at 0.001% FAR after age transformations of approximately 28 and 23 years elapsed time on the MORPH and CACD databases, respectively. Both visual and quantitative assessments show that the approach advances the state-of-the-art.

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“…In addition, Zhou et al [4] argue that occupation information influences the individual aging process and propose an occupational-aware adversarial face aging network. To make the most of the discriminative ability of GAN, Yang et al [2] put forward a multi-pathway discriminator to refine the aging/rejuvenating results. Duong et al [3] present a generative probabilistic model to simulate the aging mechanism of each age stage.…”

Section: B Age Synthesismentioning

confidence: 99%

Global and Local Consistent Wavelet-Domain Age Synthesis

et al. 2019

IEEE Trans.Inform.Forensic Secur.

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Age synthesis is a challenging task due to the complicated and non-linear transformation in human aging process. Aging information is usually reflected in local facial parts, such as wrinkles at the eye corners. However, these local facial parts contribute less in previous GAN based methods for age synthesis. To address this issue, we propose a Waveletdomain Global and Local Consistent Age Generative Adversarial Network (WaveletGLCA-GAN), in which one global specific network and three local specific networks are integrated together to capture both global topology information and local texture details of human faces. Different from the most existing methods that modeling age synthesis in image-domain, we adopt wavelet transform to depict the textual information in frequency-domain. Moreover, five types of losses are adopted: 1) adversarial loss aims to generate realistic wavelets; 2) identity preserving loss aims to better preserve identity information; 3) age preserving loss aims to enhance the accuracy of age synthesis; 4) pixel-wise loss aims to preserve the background information of the input face; 5) the total variation regularization aims to remove ghosting artifacts. Our method is evaluated on three face aging datasets, including CACD2000, Morph and FG-NET. Qualitative and quantitative experiments show the superiority of the proposed method over other state-of-the-arts.Index Terms-Age synthesis, wavelet transform, generative adversarial network, global and local features. † These authors contributed to the work equally.

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Learning Face Age Progression: A Pyramid Architecture of GANs

Cited by 188 publications

References 34 publications

Mask-Guided Portrait Editing With Conditional GANs

Mask-Guided Portrait Editing With Conditional GANs

Learning Continuous Face Age Progression: A Pyramid of GANs

Global and Local Consistent Wavelet-Domain Age Synthesis

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