Here, face images of a specific age class are translated to images of different age classes in an unsupervised manner that enables training on independent sets of images for each age class. In order ...to learn pairwise translations between age classes, we adopt the UNsupervised Image-to-image Translation framework that employs Variational AutoEncoders and Generative Adversarial Networks. By mapping face images of different age classes to shared latent representations, the most personalized and abstract facial characteristics are preserved. To effectively diffuse age class information, a pyramid of local, neighbour, and global encoders is employed so that the latent representations progressively cover an increased age range. The proposed framework is applied to the FGNET aging database and compared to state-of-the-art techniques and the ground truth. Appealing experimental results demonstrate the ability of the proposed method to efficiently capture both intense and subtle aging effects.
Continuous Face Aging Generative Adversarial Networks Jeon, Seogkyu; Lee, Pilhyeon; Hong, Kibeom ...
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
2021-June-6
Conference Proceeding
Face aging is the task aiming to translate the faces in input images to designated ages. To simplify the problem, previous methods have limited themselves only able to produce discrete age groups, ...each of which consists of ten years. Consequently, the exact ages of the translated results are unknown and it is unable to obtain the faces of different ages within groups. To this end, we propose the continuous face aging generative adversarial networks (CFA-GAN). Specifically, to make the continuous aging feasible, we propose to decompose image features into two orthogonal features: the identity and the age basis features. Moreover, we introduce the novel loss function for identity preservation which maximizes the cosine similarity between the original and the generated identity basis features. With the qualitative and quantitative evaluations on MORPH, we demonstrate the realistic and continuous aging ability of our model, validating its superiority against existing models. To the best of our knowledge, this work is the first attempt to handle continuous target ages.
Due to the continuous development of GAN, vivid faces can be generated, and the use of GAN for face aging becomes a novel trend. However, many existing works for face aging require tedious ...pre-processing of datasets. This brings a lot of computational burden and limits the application of face aging. In order to solve these problems, a face aging network is constructed using IcGAN without any data pre-processing which map a face image into personality and age vector spaces through encoders Z and Y. Different from the previous work, we make an emphasis on the preservation of both personalized and aging features. Thus, the minimize absolute reconstructing loss is proposed to optimize vector z, which can remain the personality characteristics, meanwhile preserving the pose, hairstyle and background of the input face. Additionally, we introduce a novel age vector optimization approach by classifying reconstruction loss and introduce the parameter λ which is well-balanced between large age features and subtle texture features. The experimental results demonstrate our proposed AIGAN provides better aging faces over other state-of-the-art age progression methods.
Although impressive results have been achieved for age progression and regression, there remain two major issues in generative adversarial networks (GANs)-based methods: 1) conditional GANs ...(cGANs)-based methods can learn various effects between any two age groups in a single model, but are insufficient to characterize some specific patterns due to completely shared convolutions filters; and 2) GANs-based methods can, by utilizing several models to learn effects independently, learn some specific patterns, however, they are cumbersome and require age label in advance. To address these deficiencies and have the best of both worlds, this paper introduces a dropout-like method based on GAN (RoutingGAN) to route different effects in a high-level semantic feature space. Specifically, we first disentangle the age-invariant features from the input face, and then gradually add the effects to the features by residual routers that assign the convolution filters to different age groups by dropping out the outputs of others. As a result, the proposed RoutingGAN can simultaneously learn various effects in a single model, with convolution filters being shared in part to learn some specific effects. Experimental results on two benchmarked datasets demonstrate superior performance over existing methods both qualitatively and quantitatively.
Since generative adversarial networks (GANs) were proposed in 2014, mode collapse has been a problem that affects many researchers when training GANs. With the reconstruction loss of an autoencoder, ...conditional adversarial autoencoder (CAAE) is free from mode collapse. However, its reconstruction loss will bring a saturation problem, in which the encoder maps every input image into just one latent variable. Combining the CAAE with a boundary equilibrium generative adversarial network, we propose a boundary equilibrium conditional autoencoder (BECAE) focusing on the face aging task. Our model is the first GANs that renders images through a discriminator. We also introduce some statistics to measure the level of the saturation problem. The results show that the BECAE has successfully solved the saturation problem and can generate face images of the same quality as the images generated by the CAAE.
In this paper, we present an example-based method to estimate the aging process of a human face. To tackle the difficulty of collecting considerable chronological photos of individuals, we utilize a ...two-layer strategy. Based on a sparse aging database, an EM-PCA-based algorithm with the personal guidance vector is first applied to conjecture the temporal variations of a target face. Since the subspace-based prediction may not preserve detailed creases, we propose synthesizing facial details with a separate texture dataset. Besides automatic simulation, the proposed framework can also include further guidance, e.g., parents’ impact vector or users’ indication of wrinkles. Our estimated results can improve feature point positions and user evaluation demonstrates that the two-layer approach provides more reasonable aging prediction.
Nowadays, the main personal verification system uses the photo in Thai ID card. However, the card can be used up to nine year. Consequently, there could be difference between the face on ID card and ...the current face of the cardholder. This article demonstrates the use of ArcFace + Face aging with IPCGAN to solve the problem of cross-age face comparison. Based on the experimental results, the proposed algorithm outperforms other algorithms. Moreover, to automatically extract text information from the ID card, the developed system applies OCR of Google document text detection. It was found that Thai text information obtained from the proposed system yielded 93.97% accuracy, which is higher than others.
This work presents an approach for combining texture and shape feature sets towards age-invariant face recognition. Physiological studies have proven that the human visual system can recognise ...familiar faces at different ages from the face outline alone. Based on this scientific fact, the phase congruency features for shape analysis were adopted to produce a face edge map. This was beneficial in tracking the craniofacial growth pattern for each subject. Craniofacial growth is common during childhood years, but after the age of 18, the texture variations start to show as the effect of facial aging. Therefore, in order to handle such texture variations, a variance of the well-known local binary pattern (LBP) texture descriptor, known as LBP variance was adopted. The results showed that fusing the shape and the texture features set yielded better performance than the individual performance of each feature set. Moreover, the individual verification accuracy for each feature set was improved when they were transformed to a kernel discriminative common vectors presentation. The system achieved an overall verification accuracy of above 93% when it was evaluated over the FG-NET face aging database.
Gan-Based Face Aging System with Dynamic Age Intervals Babu, D. Suresh; Dabbara, Pranathi; Purama, Vasanthi ...
2022 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON),
2022-Dec.-23
Conference Proceeding
This article explores and implements what a person looks like at different ages. Age affects every inch of the body. Along with the wisdom, experience, and accomplishments that come with aging, so ...too does our appearance. The change in our faces is of utmost importance. The facial aging system has a variety of uses, including finding lost children, chasing criminals, etc. Previous work on facial aging systems was developed by training a generator in a GAN to static ages such as 00-10, 11-20, and 21-30. The main goal of this work is to propose a model that dynamically determines age groups according to user requirements. In this paper, we implemented a model of GAN for every single age instead of groups of ages. This paper uses the Tkinter object which is a slider that allows the user to select the starting age and next age interval. The output of this model is a set of images for user-specified age groups.
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