Table 1.

Literature of GANs based on face aging methods.

GANs	Author/ year	Pros	Cons
CAAE [23]	Zhang Z et al 2017	The face age progression task was performed without the need for paired samples. At the first time, this method achieved face age progression and regression in a general method. It has the potential to assist as a general method in the case of face-age-related tasks. Thus, it can state whether the input face matches a specific age, that is the exact motive of face age estimation.	In this method, the generated output shows rough wrinkles on account of inadequate discriminative and generative capability.
Age-cGAN [22]	Antipov et al 2017	Introduced “Identity-preserving” latent vector optimization method that preserves the unique individual’s identity in the reconstruction. It is a universal method means it can be used to preserve identity not only for face aging but also in other face modifications tasks such as the addition of sunglasses, a beard, etc.	Age-cGAN losses the unique individual’s identity even before age progression/regression in almost 20% of cases, makes it impossible for practical application to progress in cross-age face verification tasks.
CycleGAN [33]	Zhu et al 2017	Works with an unpaired dataset. Being unsupervised, image quality is good in many translations of the images. Also, it works well with texture and color change.	But geometric changes show little progress.
IPcGAN [24]	Wang et al 2018	IPcGANs can be useful for multi-attribute generation tasks, such as facial expressions, hair colors, etc. It can be used for imbalanced data classification scenes. If the conditional part is removed, this framework can be used for the image translation tasks.	With a data augmentation method, aged faces generated in this method were not able to improve face recognition performance.
Age-DCGAN [38]	Liu et al 2018	Perceptual similarity loss substituted adversarial loss in GANs as the objective function.	The result on age regression was not so good, especially for grown-up males.
Contextual GAN [39]	Liu et al 2018	This method had achieved very well when the given image and target age groups were adjoining. It was because of the proposed Transition Pattern Discriminative network.	This method was slightly weak for synthesizing face images of 60+ age from children’s face images.
DualGAN [26]	Song et al 2018	The primal conditional GAN transforms an input face image to another age, based on the age condition. However, the dual conditional GAN learned to invert the task.	FT demo result outperformed Dual cGANs by 4%.
Wavelet GLCA-GAN [28]	Li et al 2019	With the introduction of the frequency domain information, the generated images were stronger also sensitive to facial texture.	The model had difficulty in learning to change the hair color.
A3GAN [40]	Liu Y et al 2019	With the use of WPT (Wavelet Packet Transform), the computational cost was significantly reduced. Thus, also improve visual fidelity.	Outcomes attained with setting ‘wWPT’ still suffered from incorrect facial attributes.
S2GAN [41]	He et al 2019	As S2 -module was orthogonal to some methods, thus reduces the computational consumption and enables continuous aging.	Whole personalized aging factors using several images for each individual at diverse ages could be established.
Triple-GAN [42]	Fang et al 2020	The triple translation helped in learning age patterns independently. Because it made the correlations among age domains.	Triple translation loss needed supervision otherwise, the distance of domains among input face image and output face image was still so far, results in messy and lost face images.
PFA-GAN [43]	Huang et al 2020	Introduced an aging smoothness metric and new age estimation loss. The PFA-GAN can be optimized in an end-to-end manner to eliminate the accumulative error.	The networks needed the source image age label at the input to attain the aging process. Also, splitting into age groups in face aging, made it difficult for end-to-end training. Therefore, the transformation between two adjoining age groups will become less clear.
AGR-GAN [44]	Yadav et al 2020	This method included age gap loss and identity loss among the input and the synthesized face images.	The synthesized face images may appear over-smoothed in some cases. This may be credited to the existence of the L1 term in the loss function that has been observed in other methods as well [37]. Another cause for it could be the inadequate amount of training images for different age groups, specifically the young and old age groups.
AMGAN [45]	Despois et al 2020	The patch-based method allowed conditional generative adversarial networks to be trained on huge face images though keeping a large batch size. This method was applied to several problems and it could be used to tackle high-resolution difficulties with limited computation resources.	A major drawback of patch-based training was that small patches may look similar such as cheek and forehead. As yet they must be aged differently. For example, horizontal and vertical wrinkles respectively.
Attention GAN [37]	Tang et al 2020	Works with the unpaired dataset, Image quality was good at the output and generates realistic and sharper images. As generators can learn on the foreground of the target domain and preserve the background of the source domain efficiently.	AttentionGAN limitation was shown by scheme A where the model could not handle a complex task for translation.
InterFace GAN [46]	Shen et al 2020	Like many methods manipulating the age, gender, presence of eyeglasses, and expression, this method can even modify the face image pose and fix the GANs artifacts which were accidentally produced.	This method may fail for long-distance manipulation due to the linear assumption.
EigenGAN [47]	He et al 2021	EigenGAN embeds one linear subspace with an orthogonal basis into each generator layer.	Discovered semantic attributes are not always the same at different training times in two cases: in gender and pose learning, Sometimes the model can discover a specific attribute but sometimes cannot, such as eyeglasses.
MTLFace [48]	Huang et al 2021	MTLFace (multi-task learning framework), able to focus on age-invariant identity-related representation and achieves notable face synthesis.	Since in general, the GANs still face the training problem.
CFA-GAN [49]	Jeon et al 2021	The new loss function for identity preservation maximizes the cosine similarity among the given input image and the synthesized identity source features.	It is observed that the age errors are comparatively high with the target ages larger than 30. It can be because of data imbalance.
pixel2style2pixel (pSp) [50]	Richardson et al 2021	The encoder can straight embed actual images into W+, with no added optimization.	The high-quality face images that are produced with the use of pre-trained StyleGAN come with a cost. This technique is limited to images that can be synthesized by StyleGAN.