A Unified Framework for Biphasic Facial Age Translation with Noisy-Semantic Guided Generative Adversarial Networks

• URL: http://arxiv.org/abs/2109.07373v1
• Date: Wed, 15 Sep 2021 15:30:35 GMT
• Title: A Unified Framework for Biphasic Facial Age Translation with Noisy-Semantic Guided Generative Adversarial Networks
• Authors: Muyi Sun, Jian Wang, Yunfan Liu, Qi Li, Zhenan Sun
• Abstract summary: Biphasic facial age translation aims at predicting the appearance of the input face at any age. We propose a unified framework for biphasic facial age translation with noisy-semantic guided generative adversarial networks.
• Score: 54.57520952117123
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Biphasic facial age translation aims at predicting the appearance of the input face at any age. Facial age translation has received considerable research attention in the last decade due to its practical value in cross-age face recognition and various entertainment applications. However, most existing methods model age changes between holistic images, regardless of the human face structure and the age-changing patterns of individual facial components. Consequently, the lack of semantic supervision will cause infidelity of generated faces in detail. To this end, we propose a unified framework for biphasic facial age translation with noisy-semantic guided generative adversarial networks. Structurally, we project the class-aware noisy semantic layouts to soft latent maps for the following injection operation on the individual facial parts. In particular, we introduce two sub-networks, ProjectionNet and ConstraintNet. ProjectionNet introduces the low-level structural semantic information with noise map and produces soft latent maps. ConstraintNet disentangles the high-level spatial features to constrain the soft latent maps, which endows more age-related context into the soft latent maps. Specifically, attention mechanism is employed in ConstraintNet for feature disentanglement. Meanwhile, in order to mine the strongest mapping ability of the network, we embed two types of learning strategies in the training procedure, supervised self-driven generation and unsupervised condition-driven cycle-consistent generation. As a result, extensive experiments conducted on MORPH and CACD datasets demonstrate the prominent ability of our proposed method which achieves state-of-the-art performance.

Related papers

• When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework and A New Benchmark [45.31997043789471]
  MTLFace can learn the age-invariant identity-related representation for face recognition while achieving pleasing face synthesis for model interpretation. We release a large cross-age face dataset with age and gender annotations, and a new benchmark specifically designed for tracing long-missing children.
  arXiv  Detail & Related papers  (2022-10-17T07:04:19Z)
• TANet: A new Paradigm for Global Face Super-resolution via Transformer-CNN Aggregation Network [72.41798177302175]
  We propose a novel paradigm based on the self-attention mechanism (i.e., the core of Transformer) to fully explore the representation capacity of the facial structure feature. Specifically, we design a Transformer-CNN aggregation network (TANet) consisting of two paths, in which one path uses CNNs responsible for restoring fine-grained facial details. By aggregating the features from the above two paths, the consistency of global facial structure and fidelity of local facial detail restoration are strengthened simultaneously.
  arXiv  Detail & Related papers  (2021-09-16T18:15:07Z)
• FP-Age: Leveraging Face Parsing Attention for Facial Age Estimation in the Wild [50.8865921538953]
  We propose a method to explicitly incorporate facial semantics into age estimation. We design a face parsing-based network to learn semantic information at different scales. We show that our method consistently outperforms all existing age estimation methods.
  arXiv  Detail & Related papers  (2021-06-21T14:31:32Z)
• Continuous Face Aging via Self-estimated Residual Age Embedding [8.443742714362521]
  We propose a unified network structure that embeds a linear age estimator into a GAN-based model. The embedded age estimator is trained jointly with the encoder and decoder to estimate the age of a face image. The personalized target age embedding is synthesized by incorporating both personalized residual age embedding of the current age and exemplar-face aging basis of the target age.
  arXiv  Detail & Related papers  (2021-04-30T18:06:17Z)
• When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework [20.579282497730944]
  MTLFace can learn age-invariant identity-related representation while achieving pleasing face synthesis. In contrast to the conventional one-hot encoding that achieves group-level FAS, we propose a novel identity conditional module to achieve identity-level FAS. Extensive experiments on five benchmark cross-age datasets demonstrate the superior performance of our proposed MTLFace.
  arXiv  Detail & Related papers  (2021-03-02T07:03:27Z)
• Continuous Face Aging Generative Adversarial Networks [11.75204350455584]
  Face aging is the task aiming to translate the faces in input images to designated ages. Previous methods have limited themselves only able to produce discrete age groups, each of which consists of ten years. We propose the continuous face aging generative adversarial networks (CFA-GAN)
  arXiv  Detail & Related papers  (2021-02-26T06:22:25Z)
• PFA-GAN: Progressive Face Aging with Generative Adversarial Network [19.45760984401544]
  This paper proposes a novel progressive face aging framework based on generative adversarial network (PFA-GAN) The framework can be trained in an end-to-end manner to eliminate accumulative artifacts and blurriness. Extensively experimental results demonstrate superior performance over existing (c)GANs-based methods.
  arXiv  Detail & Related papers  (2020-12-07T05:45:13Z)
• Age Gap Reducer-GAN for Recognizing Age-Separated Faces [72.26969872180841]
  We propose a novel algorithm for matching faces with temporal variations caused due to age progression. The proposed generative adversarial network algorithm is a unified framework that combines facial age estimation and age-separated face verification.
  arXiv  Detail & Related papers  (2020-11-11T16:43:32Z)
• Dual-Attention GAN for Large-Pose Face Frontalization [59.689836951934694]
  We present a novel Dual-Attention Generative Adversarial Network (DA-GAN) for photo-realistic face frontalization. Specifically, a self-attention-based generator is introduced to integrate local features with their long-range dependencies. A novel face-attention-based discriminator is applied to emphasize local features of face regions.
  arXiv  Detail & Related papers  (2020-02-17T20:00:56Z)
• Exploiting Semantics for Face Image Deblurring [121.44928934662063]
  We propose an effective and efficient face deblurring algorithm by exploiting semantic cues via deep convolutional neural networks. We incorporate face semantic labels as input priors and propose an adaptive structural loss to regularize facial local structures. The proposed method restores sharp images with more accurate facial features and details.
  arXiv  Detail & Related papers  (2020-01-19T13:06:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.