Face aging has attracted widespread attention in recent years, but most studies are based on the same emotional situation. Is the same person's aging in different emotional situations the same? To ...solve the above confusion, this paper proposes a novel face aging model DEF-Net, which consists of two parts: different emotional learnings (Emotion-Net) and face aging (Age-Net). Given a target emotion category, DEF-Net first assists the image from the original dataset to learn the emotion features through Emotion-Net and the generated dataset is used as the inputs of Age-Net. At the same time, multiple loss functions are used to ensure that the crucial information of the original image is not lost. Secondly, Age-Net, which has been pre-trained on the original dataset, began to adopt the generated dataset to learn the aging distribution under different emotions. Designed loss functions are utilized to ensure that the realistic target images generated by Age-Net do not lose the learned emotional characteristics. Finally, extensive experiments are used to verify the performance of DEF-Net. Compared with other state-of-the-art methods: (1) DEF-Net can learn different facial emotions across different datasets and generate corresponding realistic aging images; (2) the results achieved by our DEF-Net are demonstrated to be better than those by the model that performs face aging first and then learns different emotional characteristics.
Face aging aims to estimate aged facial textures given a certain face image. A number of 2D face-aging methods have been developed, but there have been few studies on 3D face aging, which would be ...valuable in several real-world applications. The lack of 3D face-aging data has had a significant impact on the development of 3D face aging, but we hypothesized that the large amounts of 2D face-aging data on the internet could be leveraged for 3D aged facial textures. In this paper, we propose a novel 3D aging framework, which we call UV-transformation texture estimation based on generative adversarial networks (UVTE-GAN), to achieve 3D face aging. Specifically, the proposed framework has three parts: 1) a 3D vertex and texture estimator, which accurately estimates the face's spatial vertices and textures; 2) a texture-aging GAN, which is responsible for aging the estimated texture map via adversarial learning; and 3) a 2D & 3D rendering rebuilder, which recovers 2D & 3D faces using the estimated facial vertex map and aged facial texture map. In addition, we also design a plugin layer that allows us to train the whole model in an end-to-end manner. Experimental results demonstrate the effectiveness of the proposed method in synthesizing visually pleasing 3D aged face pictures, and state-of-the-art performance is achieved on several public datasets.
Despite the recent advances of face age transformation in the age accuracy of face synthesis or the integrity of identity information, only age used as the condition of aging is insufficient to ...depict the real aging process. In this work, we propose a novel GAN-based identity-preserving aging method with Multi-scale attention and Multi-attribution fusion (named M2aging) to address this problem. In particular, we design a High-fidelity Multi-scale Attention(HiMA) module to capture minute and local features at different scales, improving the model's identity-preserving ability. In addition, a Multi-attribution Fusion Modulator (MFM) is proposed by fusing age, gender, and race attributions to control the aging process, which enhances attribution preservation without introducing any additional classifiers and training losses. Experimental results show the proposed M2aging outperforms other SOTA techniques in terms of identity preservation and accurate age conversion. The ablation experiments manifest that HiMA can effectively heighten the fidelity of the transformed images, and MFM can effectively control the aging process according to age, gender, and race.
Toward Quantifiable Face age Transformation Lin, Ling; Zhu, Congcong; Zhou, Lin ...
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
2024-April-14
Conference Proceeding
Odprti dostop
Face aging is a highly complex process that includes intricate aging patterns. Previous works condition aging patterns utilizing one-hot or artificial predefined distributions. Nevertheless, ...different age groups show different intraclass variation in appearance. This makes it difficult for previous methods to discriminately express differences in apparent age across all age groups leading to degradation of model performance. To address this issue, we propose the Shapley Value Quanti-zation(SVQ) module and the Differentiated Age Embedding Transformation(DAT) module for calculating age differences and performing age modulation. Specifically, the SVQ module quantifies the contribution of different attributes to age using Shapley values. This allows us to obtain adaptive age distributions for different age groups. Subsequently, the DAT module takes a target age vector, sampled from the target age distribution quantized by SVQ, and modulates the age representation of the generated image. Experimental results show outperforms our approach in comparison to the state of the arts face aging methods by a large margin.
Existing face aging (FA) approaches usually concentrate on a universal aging pattern, and produce restricted aging faces from one-to-one mapping. However, the diversity of living environments impact ...individuals differently in their oldness. To simulate various aging effects, we propose a multimodal FA framework based on face disentanglement technique of age-specific and age-irrelevant information. A Variational Autoencoder (VAE)-based encoder is designed to represent the distribution of the age-specific attributes. To capture the age-irrelevant features, a cycle-consistency loss of unpaired faces is utilized among various age spans. The extensive experimental results demonstrate that the sampled age-specific codes along with an age-irrelevant feature make the multimodal FA diverse and realistic.
Age is one of the most important biological characteristics of the human face. The increase of age coincides with the increase of the aging degree of the face. Face aging synthesis is attracting ...increasingly more attention from domestic and overseas scholars in the computer vision and computer graphics fields, and it can be integrated into the basic research of face correlation, such as cross‐age face analysis and age estimation. At present, some achievements have been made in face aging synthesis research; however, it is still an urgent problem to reduce the number of parameters and computational complexity of the network while ensuring the aging effect. Therefore, a new face aging algorithm is proposed in this article. Unlike the previous methods of aging process simulation, we introduce an assisted age classification network based on the principle of homology continuity, which is more in line with the human cognition process. After pretraining, the result of age classification is improved, and the pretraining model is then added to the framework of aging face generation for fine‐tuning to constrain the generated aging face, which can improve the aging accuracy of the generated image. Furthermore, we reconstruct the input face by using the age tag of the input face and the synthesized aging face and maintain the identity invariance in the face aging process by minimizing the reconstruction loss. The experimental results show that the method proposed in this article produces a considerable effect of face aging and significantly reduces the number of parameters and the complexity of computational.
This paper presents a novel subject-dependent deep aging path (SDAP), which inherits the merits of both generative probabilistic modeling and inverse reinforcement learning to model the facial ...structures and the longitudinal face aging process of a given subject. The proposed SDAP is optimized using tractable log-likelihood objective functions with convolutional neural networks (CNNs) based deep feature extraction. Instead of applying a fixed aging development path for all input faces and subjects, SDAP is able to provide the most appropriate aging development path for individual subject that optimizes the reward aging formulation. Unlike previous methods that can take only one image as the input, SDAP further allows multiple images as inputs, i.e. all information of a subject at either the same or different ages, to produce the optimal aging path for the given subject. Finally, SDAP allows efficiently synthesizing in-the-wild aging faces. The proposed model is experimented in both tasks of face aging synthesis and cross-age face verification. The experimental results consistently show SDAP achieves the state-of-the-art performance on numerous face aging databases, i.e. FG-NET, MORPH, aging faces in the wild (AGFW), and cross-age celebrity dataset (CACD). Furthermore, we also evaluate the performance of SDAP on large-scale Megaface challenge to demonstrate the advantages of the proposed solution.
Face aging (FA) for young faces refers to rendering the aging faces at target age for an individual, generally under 20s, which is an important topic of facial age analysis. Unlike traditional FA for ...adults, it is challenging to age children with one deep learning-based FA network, since there are deformations of facial shapes and variations of textural details. To alleviate the deficiency, a unified FA framework for young faces is proposed, which consists of two decoupled networks to apply aging image translation. It explicitly models transformations of geometry and appearance using two components: GD-GAN, which simulates the Geometric Deformation using Generative Adversarial Network; TV-GAN, which simulates the Textural Variations guided by the age-related saliency map. Extensive experiments demonstrate that our method has advantages over the state-of-the-art methods in terms of synthesizing visually plausible images for young faces, as well as preserving the personalized features.
•Face aging for young faces refers to rendering the aging faces at target age for an individual, generally under 20s.•Realistic generated face portrayals are helpful for finding missing children by offering probable aging appearances.•Both facial shapes deform and textures vary in face aging for young child.•During facial image synthesizing, personal identity should be preserved.
Face aging has received significant research attention in recent years. Although great progress has been achieved with the success of Generative Adversarial Networks (GANs) in synthesizing realistic ...images, most existing GAN-based face aging methods have two main problems: 1) unnatural changes of high-level semantic information due to the insufficient consideration of prior knowledge of input faces, and 2) distortions of low-level image content (e.g. modifications in age-irrelevant regions). In this article, we introduce A 3 GAN, an Attribute-Aware Attentive face aging model to address the above issues. Facial attribute vectors are regarded as the conditional information and embedded into both the generator and discriminator, encouraging synthesized faces to be faithful to attributes of corresponding inputs. To improve the visual fidelity of generation results, we leverage the attention mechanism to restrict modifications to age-related areas and preserve image details. Unlike previous works with attention modules, we introduce face parsing maps to help the generator distinguish image regions of interest and suppress attention activation elsewhere. Moreover, the wavelet packet transform is employed to capture textural features at multiple scales in the frequency space. Extensive experimental results demonstrate the effectiveness of our model in synthesizing photo-realistic aged face images and achieving state-of-the-art performance on popular datasets.
An innovative approach is proposed that leverages a perturbation explainable system within the Conditional Adversarial Autoencoder (CAAE) framework. The incorporation of the perturbation-based ...explainable system in the CAAE model harnesses the explanatory power of Local Interpretable Model-Agnostic Explanations (LIME). LIME generates perturbations in the latent space of the CAAE and provides insightful explanations for the discrepancies between fake and real face images. By indicating the areas that contribute most significantly to the aging process, LIME guides the adversarial training process to focus on those aspects, resulting in corrective feedback to the discriminator. The performance of the proposed framework, against state-of-the-art methods, is assessed by objective figures of merit demonstrating superior results in face aging.
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