Human age synthesis, also known as age progression/regression, has captured much attention in many real-world applications such as finding missing children, age estimation, cross-age face verification, social entertainment, etc. It aims to aesthetically rerender a given face with natural aging effects but still preserve personality. Impressive progress has been made on age synthesis in recent years and many methods [1, 2, 3, 4, 5] have been proposed. However, large-scale age synthesis is still a very challenging problem for several reasons. 1) Rigid requirement to the training and testing datasets, i.e., most existing works require face images of the same subject at different ages. 2) Faces captured in the wild are usually influenced by the large variations of illumination, pose, resolution and expression. 3) Different subjects have different aging processes. There are many internal and external influential factors, e.g., gene, gender, living style.
In prior works, physical modeling based methods and prototype-based methods are mainly used for age synthesis. Physical modeling based methods build computational models for age progression/regression to simulate human biological and aging mechanisms of the cranium, muscles or facial skin etc. Although rich and obvious textures can be generated, these approaches depend on many face sequences of the same subject covering a long age span, which is hard and expensive to obtain. Besides, in prototype-based methods, only texture synthesis is considered. Training data is divided into different age groups. Average face of each age group is regarded as its prototype, thus the aging pattern is the difference between prototypes of target and source age groups. However, prototype-based approaches may ignore identity and different subjects share the same aging pattern.
Recently, deep learning based methods have made a breakthrough in computer vision. Training with large-scale data, deep neural networks substantially show better performance than previous methods in image generation. Generative Adversarial Networks (GANs) as a specific type of deep neural network, has proven capable of generating photo-realistic images and is therefore introduced to deal with age synthesis [7, 8, 1, 2, 3, 4, 5]. However, the generated results of existing GAN based methods are often over-smoothed and lack of detailed texture information. Fine-grained age synthesis remains an open and challenging task.
We tackle this fine-grained age synthesis from two aspects: modeling aging patterns of age-sensitive areas with local networks and introducing wavelet transform to age synthesis. In the first place, aging texture information is usually reflected in the local facial parts, such as wrinkles at the eye corners or on the forehead. However, existing GAN based models cannot fully capture these rich textures in the local facial parts. Secondly, most current age synthesis methods only depend on modeling aging process in image-domain. Thus the outputs of these methods tend to be over-smoothed and lack of textural details. Since the subtle texture information is more salient and robust in frequency-domain, we introduce wavelet transform to age synthesis.
In this paper, a novel Wavelet-domain Global and Local Consistent Age Generative Adversarial Network (WaveletGLCA-GAN) is proposed for fine-grained age synthesis. It consists of four parts: wavelet-domain global and local consistent age generator, discriminator, age evaluation network, identity evaluation network. Fig. 1 and Fig. 2 depict the rejuvenating results and framework of WaveletGLCA-GAN, respectively.
The wavelet-domain global and local consistent age generator (WaveletGLCA-G) takes a random age face as an input and predicts the corresponding wavelet coefficients before reconstructing the target age output. Apart from one global specific network like previous works, three local specific networks are imposed to capture the aging texture information in forehead, eyes and mouth parts, respectively. Besides, the wavelet coefficient prediction network is employed to predict wavelet coefficients of the target age faces. Instead of manipulating a whole face, our WaveletGLCA-G learns the residual face, which can accelerate the convergence while preserving the identity and background information. The discriminator is proposed to harness target age information and tries to distinguish the synthesized faces from the real target age faces. Through adversarial training, the WaveletGLCA-G is forced to produce photo-realistic target age images.
Aging accuracy is one of the two critical requirements in age synthesis. Thus, we adopt the age evaluation network to evaluate and reinforce the aging accuracy in age synthesis. The age evaluation network takes the generated face as an input and estimates its age. Meanwhile, identity preserving performance is the other critical requirement in age synthesis. An identity evaluation network is introduced to evaluate and enhance the identity preserving performance in age synthesis. It takes the generated and participant faces as inputs and computes their similarity scores to evaluate how much identity information can be retained.
The main contributions of this work are as follows:
1) A global and local consistent age generator is proposed for deep facial age synthesis, in which three local specific networks and one global specific network are integrated together to capture both global topology and local texture information of human faces.
2) Different from the most existing studies that model age synthesis in image-domain, we transform age synthesis to a wavelet coefficient prediction task in frequency-domain. To the best of our knowledge, this is the first attempt to transform age synthesis to wavelet coefficient prediction task in GAN-based framework for age synthesis.
3) The proposed WaveletGLCA-GAN simultaneously achieves age progression and regression in the same framework with the given age labels. Experimental results show that the proposed approach outperforms state-of-the-art methods in terms of qualitative and quantitative metrics.
This paper is an extension of our previous method GLCA-GAN . Apart from providing more in-depth analysis and more extensive experiments, the major difference between this paper and its previous version lies in three-folds: 1) Age synthesis is extended from image-domain generative adversarial network (GLCA-GAN) to wavelet-domain generative adversarial network (WaveletGLCA-GAN) with the impose of frequency-domain information which is more sensitive to texture details. 2) Age synthesis is transformed into wavelet coefficients prediction task. Wavelet coefficient prediction network as well as wavelet reconstruction networks are used to capture and model the delicate texture information in wavelet-domain. Besides, higher-resolution age synthesis is achieved with pixel-size extended from to . 3) Age estimation and identity verification are proposed to evaluate the aging accuracy and identity preserving performance of WaveletGLCA-GAN.
Ii Related Work
Ii-a Image-to-image Translation
Age synthesis is a subproblem of image-to-image translation which transforms the input image from the source domain to the target domain with the original structure or semantics preserving. In recent years, image translation has been widely investigated. A host of recent works have explored the ability of Generative Adversarial Networks (GANs) for image-to-image translation [10, 11]. With a generator and a discriminator, GANs play a min-max game to learn the target image prior. pix2pix  learns image-to-image translation in a supervised manner using cGANs , which requires paired data samples. StarGAN 
adds a mask vector to the domain label, which enables joint training between multiple domains. To alleviate the requirement to paired data, unpaired image-to-image translation has been proposed. Cycle-consistent Adversarial Networks (CycleGANs) is successfully applied for unpair image-to-image translation. Recently a Couple-Agent Pose-Guided Generative Adversarial Network (CAPG-GAN)  is proposed for arbitrary view face synthesis which combines prior domain knowledge of pose and local structure of face to reinforce the realism of results.  introduces the idea of asymmetric style transfer and proposes PairedCycleGAN, which involves two asymmetric networks for makeup transfer and removal.
Ii-B Age Synthesis
The facial age synthesis methods can be mainly classified into three categories: physical modeling based, prototype based and deep learning based methods.
In the early time, physical modeling based methods and prototype based methods are mainly adopted in age synthesis. The physical modeling based methods [18, 19, 20, 21, 22] simulate age progression/regression by modeling human biological and physical mechanisms, e.g., muscles, facial contours. However, this kind of approaches need abundant face sequences of the same subject covering a long age span, which is expensive and hard to obtain. Besides, prototype-based approaches [23, 24] regard the average face of each age group as its prototype, thus the aging pattern is the difference between the prototypes of target and source age groups. However, prototype-based approaches ignore identity information. To handle this problem, Shu et al.  propose an age synthesis method based on dictionary learning to reconstruct the aging face.
propose a recurrent neural network to model a smoother face aging process. Zhang et al.
apply Conditional Adversarial Autoencoder (CAAE) to synthesize target age faces with target age labels. In addition, Zhou et al. 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.  put forward a multi-pathway discriminator to refine the aging/rejuvenating results. Duong et al.  present a generative probabilistic model to simulate the aging mechanism of each age stage. Wang et al.  impose an identity-preserved term and an age classification term into age synthesis and propose the Identity-Preserved Conditional Generative Adversarial Networks (IPCGANs). However, the age synthesis results of existing methods are still over-smoothed and lack of detailed texture information, which hurts the aging accuracy and identity preserving performance.
Age synthesis aims to synthesize a target age face image from a given age face image . Our goal is to learn such a synthesizer that can infer the corresponding age images. The overall framework of our proposed Wavelet-domain Global and Local Consistent Age Generative Adversarial Network (WaveletGLCA-GAN) is depicted in Fig. 2, which mainly consists of four parts: wavelet-domain global and local consistent age generator, discriminator, age evaluation network, identity evaluation network.
In WaveletGLCA-GAN, age information is encoded into the age lable , which is one-hot vector with the target age being 1. Given an input age face , forehead, eyes and mouth local patches , , are first cropped according to the five facial landmarks. Then the age label is concatenated to both and three local facial patches , ,
. The new global tensorand three local tensor , , are fed into WaveletGLCA-G to syhthesize the target age image . Five types of losses are adopted to supervise the WaveletGLCA-G to achieve accurate age generation under the premise of preserving the background and identity information.
This section firstly introduces the architecture of the WaveletGLCA-G, discriminator, age evaluation network and identity evaluation network. Then the details of five supervised loss functions, including conditional adversarial loss, identity preserving loss, age preserving loss, pixel-wise loss and total variation regularization, are presented later.
Iii-a Network Architecture
Iii-A1 Architecture of WaveletGLCA-G
The wavelet-domain global and local consistent age generator (WaveletGLCA-G) takes the source age faces as inputs and synthesize the corresponding target age faces. As shown in Fig. 3, our WaveletGLCA-G consists of five subnetworks: global specific network, local specific networks, feature fusion network, wavelet coefficient prediction network as well as wavelet reconstruction network. The GSNet and three LSNets learn the feature embeddings of the whole facial topology and the subtle textures of forehead, eyes and mouth simultaneously. Then the feature fusion network fuses the four feature embeddings of the global and local networks. After that, the wavelet coefficient prediction network estimates the wavelet coefficients by four individual subnetworks. Finally, according to the predicted coefficients, the wavelet reconstruction network reconstructs the target age face. Instead of manipulating a whole face, our WaveletGLCA-G learns the residual face, which accelerates the convergence of WaveletGLCA-GAN while preserving the identity and background information.
As shown in Fig. 3, we concatenate the given image , the target age label as the input of WaveletGLCA-G and synthesize the target age image .
where is the parameter of WaveletGLCA-G. In this paper, we choose the image size of 224 and divide the ages of subjects into four age groups, thus . The detailed architecture of WaveletGLCA-G is shown in Table I.
|Feature Fusion Net|
|Wavelet Prediction Net|
Specifically, the global specific network takes the whole image concatenated with the target age label as the input . An encoder-decoder architecture with four strided convolutional layers, two residual blocks and two deconvolutional layers are exploited to extract and transform the global feature maps . In addition, the three local specific networks take forehead, eyes and mouth local patches concatenated with the target age label as the inputs and learn a separate set of filters to extract and transform the corresponding local feature maps . All the local specific networks adopt the encoder-decoder architecture with three strided convolutional layers, two residual blocks and one deconvolutional layer.
The feature fusion network fuses the four feature embeddings of global and local networks by two residual blocks. Then the wavelet coefficient prediction network takes the output of the feature fusion network as the input and predicts four specific wavelet coefficients by four individual subnetworks. All the predicted coefficients are half the size of the input, i.e, .
Finally, the wavelet reconstruction network reconstructs the target age face according to the coefficients predicted by the wavelet coefficient prediction network. The wavelet reconstruction network is a deconvolutional layer with fixed parameters.
Iii-A2 Architecture of Discriminator
To exploit the prior domain knowledge of ages, we impose age labels into the discriminator to further force the generation of age-specific faces. is the parameter of the proposed discriminator. Particularly, both the input face and the synthetic face with the target age label are treated as negative samples, while the real face with
is the positive sample. The discriminator takes them as the inputs and outputs a scalar, representing the probability that the inputs come from the real data. Based on the GAN principle, our discriminatorforces the WaveletGLCA-G to synthesize realistic and plausible target age faces, which are indistinguishable with the real faces. The detailed architecture of our discriminator is shown in Table II.
Iii-A3 Architecture of Identity Evaluation Network
An identity evaluation network is employed to preserve the identity information in age synthesis. It takes the generated face and the input face as the inputs and computes their similarity scores to evaluate how much identity information is preserved. We choose a pretrained Light CNN  as the identity evaluation network and fix the parameters during training procedure.
Iii-A4 Architecture of Age Evaluation Network
An age evaluation network is introduced in WaveletGLCA-GAN to reinforce the aging accuracy in age synthesis. It takes the generated face as the input and estimate the corresponding age to evaluate how much age-related information has been learned. We choose a pretrained VGG16  as the age evaluation network and fix the parameters during training procedure.
Iii-B Training Losses
A weighted sum of five losses is imposed to supervise the proposed WaveletGLCA-GAN in an end-to-end manner, including conditional adversarial loss, identity preserving loss, age preserving loss, pixel-wise loss and total variation regularization.
Iii-B1 Conditional Adversarial Loss
To make the synthesized images indistinguishable from the real data and incorporate prior domain knowledge (age information from target age group), the adversarial loss conditioned on the target age label is introduced to our WaveletGLCA-GAN. In addition, for avoiding producing artifacts as in , our discriminator distinguishes the local image patches separately. The adversarial loss is formulated as:
where denotes the real face of age group . Parameters of the generator and the discriminator are trained alternately to optimize the min-max problem.
Iii-B2 Identity Preserving Loss
It is crucial to keep identity information in the age synthesis. However, the synthetic faces based on GAN are usually close to the real data only in pixel space, not in semantic space. Hence, we introduce an identity preserving loss to our model. Following , the identity preserving loss is formulated as:
where and denote the feature extractors of the fully-connected layer and the last pooling layer of the pre-trained light CNN-29 network  respectively.
Iii-B3 Age Preserving Loss
Age accuracy is another key issue of age progression/regression. In our model, an age preserving loss based on the pre-trained VGG16 structure is utilized to enhance the age accuracy of the synthetic face, which can be formulated as:
denotes the output of the final softmax layer of VGG16,denotes the age class of the i-th synthetic face. When is equal to the , , otherwise .
Iii-B4 Pixel-wise Loss
By minimizing the adversarial, identity preserving and age preserving losses, can generate photo-realistic faces with accurate identity and age information. However, minimizing these losses can’t guarantee the background well preserved. Since there is no ground-truth target face in age synthesis, a pixel-wise loss between the syhthetic and the input faces is adopted to preserve the background.
where , , are the channel, height and width of the image respectively. It is worth noting that Eq. (5) forces the synthetic face image to be similar with the input face image. Thus we update this pixel-wise loss at every 10 iterations to balance the age synthesis and the background preservation.
Iii-B5 Total Variation Regularization
The images generated by GAN usually have some ghosting artifacts , which deteriorates visualization as well as recognition performance. A total variation regularization term is thus imposed to remove the unfavorable artifacts.
Iii-B6 Overall Loss
Finally, the total supervised loss is a weighted sum of the above five losses. and are trained alternately to optimize the min-max problem. Taking all loss functions together, the overall loss can be written as:
where , , , , are trade-off parameters that control the relative importance among conditional adversarial loss, identity preserving loss, age preserving loss, pixel-wise loss and total variation regularization.
WaveletGLCA-GAN provides a flexible way to rerender the input face to any age group controlled by the age label. It produces photo-realistic faces with accurate identity and age information. In the following subsections, we begin with an introduction of datasets and settings. Then we demonstrate the superiority of our WaveletGLCA-GAN on both qualitative visualization and quantitative face verification and age estimation. Lastly, we conduct ablation study to demonstrate the benefits gained from local specific networks and wavelet coefficient prediction network.
Iv-a Datasets and Settings
CACD2000 : The CACD2000 dataset contains 163,446 color images of 2,000 celebrities with aging range from 14 to 62 years old. It has more than 80 images for per subject on average. The maximum age gap between images for the same subject is 10 years. With variations of age in the wild, it has been widely used to evaluate age synthesis and age invariant verification performance under unconstrained environments. Since the face images in CACD2000 are collected from the web and contain variations in illumination, pose, expression, etc., it is extremely challenging for age synthesis task.
Morph : The Morph dataset is the largest public available dataset for evaluating age synthesis, age estimation and age invariant verification in the constrained setting, which contains 55,349 color images of 13,672 subjects with age and gender information. The subject ages of Morph range from 16 to 77 years old. It is about four images of each subject on average in the Morph dataset.
FG-NET : The FG-NET dataset is a popular benchmark for age synthesis, age estimation and age invariant verification, which only contains 1,002 images of 82 subjects. The ages of FG-NET range from 0 to 69, while the maximum age gap between images for the same subject is 54 years. We adopt FG-NET as testing set to make fair comparisons with prior works.
Five-fold cross-validation is conducted on CACD2000 and Morph. In particular, we divide the dataset into five folds with one fold for testing and the other four folds for training. On CACD2000, each fold contains 400 subjects and the face images in each fold are divided into four age groups: 14-30, 31-40, 41-50, 51-62 with about 9,536, 8,507, 7,890 and 5,959 face images, respectively. On Morph, the face images in each fold are divided into four age groups: 16-30, 31-40, 41-50, 51-77 with about 4,946, 3,207, 2,266 and 616 face images. Note that, for the settings of the two datasets, there are no overlap subjects between the training and testing sets.
Iv-A2 Experimental Settings
The identity evaluation network is pretrained on MSCeleb-1M . The age evaluation network is pretrained on IMDB-WIKI , while fine-tuned on the corresponding training sets. During training procedure, we fix the parameters of the identity evaluation network and age evaluation network. We utilize multi-task cascaded CNN  to detect and align face images. Then all the images are resized into 224
224 as the inputs. Five facial landmarks are utilized to crop three subregions of forehead, eyes and mouth. Our model is implemented with Pytorch. During training, we choose Adam optimizer withof 0.5, of 0.99, learning rate of , and the batch size is set to 8. For CACD2000, the trade-off parameters , , , and are set to 1.00, 0.01, 120.00, 45.00 and 0.00003, respectively. While for Morph, they are set to 1.00, 0.01, 80.00, 45.00 and 0.0001, respectively. In addition, we update pixel-wise loss at every 10 iteration, and update the discriminator for every 2 generator iterations.
Iv-B Qualitative Evaluation of WaveletGLCA-GAN
In this subsection, we present and compare the visualization results against state-of-the-art age synthesis methods.
Iv-B1 Results of Age Progression and Regression
WaveletGLCA-GAN can synthesize any age faces controlled by the age labels. Hence, it is able to achieve both age progression and regression. We firstly show the age progression results on CACD2000 and Morph in Fig. 5 and Fig. 7, respectively. The first column of each subject is the input face under 30 years old. The second, third and fourth columns are the synthetic results of WaveletGLCA-GAN for 31-40, 41-50 and 51-62(77) in turn. We can see that as the age labels increasing, the synthetic results become older and older. Specifically, nasolabial folds and the lines at the sides of the eyes are deepened, white hairs and beards emerge and gradually increase. Meanwhile, the age regression results on CACD2000 and Morph are shown in Fig. 6 and Fig. 8, respectively. The first column of each subject is the input beyond 51 years old. The second, third and fourth columns are the rejuvenating results of WaveletGLCA-GAN for 41-50, 31-40 and 14(16)-30 in turn. Obviously, with the age label decreasing, the white beards and hairs are gradually turned into black, dry skin begin to restore elasticity and wrinkles are reduced or even disappear.
In addition, the changes of the synthetic results are consecutive and consistent with the age label changes. Fig. 9 details the changes of synthetic results in the age rejuvenating process. In Fig. 9(a), the hairline gradually becomes lower, the hair darkens and thickens. Fig. 9(b) shows that with the age labels decreasing, the lines at the sides of the eyes get smoother, the eyes get bigger and brighter. In Fig. 9(c), the beards become less and turn black from white. Fig. 9(d) demonstrates the rejuvenating consistency of the WaveletGLCA-GAN. Besides, as is shown in Fig. 10, the WaveletGLCA-GAN is robust to pose, expression and illumination variations. For example, the occlusion, i.e., microphone, glasses, sunglasses, hat and makeup, is also well preserved in the age rejuvenating process. The proposed WaveletGLCA-GAN also has pretty generalization performance. Fig. 11 presents the face rejuvenating results with the sketch face images as the inputs.
Iv-B2 Comparison with Prior Works
Different from the most previous works, which only focus on the age synthesis of cropped faces, our proposed WaveletGLCA-GAN can produce the entire face images, including complete foreground and background. We compare the aging results of WaveletGLCA-GAN with different methods, including HFA: hidden factor analysis , FT demo: Face Transformer demo , CDL: coupled dictionary learning , RFA: recurrent face aging , CAAE: conditional adversarial autoencoder , C-GAN: contextual generative adversarial nets , GLCA-GAN: global and local consistent age generative adversarial networks , Yang et.al  as well as an popular mobile aging application, i.e. AgingBooth . Fig. 12 depicts the comparisons. Note that our WaveletGLCA-GAN is trained only on CACD2000 and tested on FG-NET. For fair comparison, we choose the same faces as their papers, and directly cite their synthetic results. As respected, WaveletGLCA-GAN obtains the best visual results with both backgrounds and foregrounds well preserved, while [37, 1, 38, 25, 8, 39] only generate cropped faces. Compared with previous best method , the synthetic images of our WaveletGLCA-GAN are clearer and have smaller color deviation, which is benefit from the introduce of the residual faces. Besides, compared with Yang et.al , our WaveletGLCA-GAN also truly simulates the changes of hairlines, i.e., the hairlines get higher in aging process as shown in Fig. 12.
Iv-C Quantitative Evaluation of WaveletGLCA-GAN
Age accuracy and identity preserving are two underlying requirements for age synthesis. We conduct age estimation and face verification to quantitatively evaluate how much age information and identity information are predicted or preserved. Specifically, if the generated images can result in better aging accuracy, more precise age information is predicted. If the generated images result in better verification accuracy, more identity information is preserved during aging/rejuvenating process.
Iv-C1 Age Estimation
Different subjects have different aging processes, but the overall trends of the synthetic faces should be robust and consistent with the real data. Following , we apply the online face analysis tool of Face++  to estimate the ages of synthetic faces. Table III shows the age estimation results for different age groups on CACD2000. We choose faces under 30 years old (age group0, AG0) as the input test images, synthesize faces in 31-40 (AG1) 41-50 (AG2) and 51-62 (AG3), and calculate the average age estimated by . As shown in Table III, the mean values of the natural faces for 31-40, 41-50 and 51-62 are 39.15, 47.14 and 53.87, respectively, while the mean values of the generated results of WaveletGLCA-GAN are 37.56, 48.13 and 54.17. In addition, we compare WaveletGLCA-GAN with CAAE , Yang et.al  and GLCA-GAN  on CACD2000, and observe that the results of WaveletGLCA-GAN are closer to the natural faces.
Meanwhile, Table IV presents the age estimation results of different methods on Morph. We choose faces under 30 years old (age group0, AG0) as the input test images, synthesize faces in 31-40 (AG1) 41-50 (AG2) and 51-77 (AG3), and calculate the average age estimated by . The mean values of the natural faces for 31-40, 41-50 and 51-67 are 38.59, 48.24 and 58.28 respectively, while the mean values of the synthetic results of WaveletGLCA-GAN are 38.36, 46.90 and 59.14. In addition, we compare WaveletGLCA-GAN with CAAE , Yang et.al  and GLCA-GAN  on Morph, and observe that the age accuracy of WaveletGLCA-GAN outperforms other methods.
Iv-C2 Face Verification
|Test FaceAG1||97.71||99.94||Test FaceAG2||98.85||100|
|Test FaceAG2||96.07||99.92||Test FaceAG1||98.91||99.83|
|Test FaceAG3||95.25||99.12||Test FaceAG0||98.53||-|
We evaluate the identity preserving performance of WaveletGLCA-GAN by face verification for both age progression and regression. In the age progression setting, for each testing face, we evaluate the verification rate between the input image and its corresponding age synthetic result: [test faceAG1], [test faceAG2] and [test faceAG3]. Following , we also conduct face verification among the synthetic faces: [AG1AG2], [AG1AG3] and [AG2AG3]. Similar with age progression, in the age regression, we evaluate on [test faceAG2], [test faceAG1], [test faceAG0], [AG2AG1], [AG2AG0] and [AG1AG0]. For CACD2000, we randomly choose negtive pairs, and make . For Morph, we randomly choose negative pairs, and make , due to the lack of old age images (about 616 images of 51-77 in each fold). Light CNN  is employed as the feature extractor in our experiments and we report TAR@FAR= on both CACD2000 and Morph. Table V presents the face verification results.
Iv-D Ablation Study
Iv-D1 Contribution of Local Specific Networks
To prove the effectiveness of the proposed three local specific networks, we compare the age accuracy of WaveletGLCA-GAN with Wavelet-GAN (The model only use global specific network to synthesize target age faces and keep others consistent with WaveletGLCA-GAN). As Tables III and IV shown, WaveletGLCA-GAN is superior to Wavelet-GAN in age accuracy.
Iv-D2 Contribution of Wavelet Coefficient Prediction Net
One assumption of WaveletGLCA-GAN is that the introduced of wavelet transform advances the generation of more subtle age-related texture information. Fig. 13 presents the visual comparisons between WaveletGLCA-GAN and GLCA-GAN , which is without wavelet coefficient prediction net. We observe that the synthetic faces by WaveletGLCA-GAN are more photo-realistic. With wavelet coefficient prediction net, more detailed and clear texture information is produced. Furthermore, Tables III and IV show WaveletGLCA-GAN outperforms GLCA-GAN in age accuracy.
This paper proposes a Wavelet-domain Global and Local Consistent Age Generative Adversarial Network (WaveletGLCA-GAN) method to synthesize a face conditioned on the age labels. WaveletGLCA-GAN simultaneously achieves age progression and regression with the given age labels and generates favorable results. With one global specific network and three local specific networks, both global topology information and local texture details of the input faces can be captured. By introducing the frequency domain information, the synthesized results are clearer and more sensitive to facial texture. Five types of losses are adopted to supervise the synthesizer to achieve accurate age generation under the premise of preserving the identity information. Extensive experimental results on age synthesis, age estimation and face verification demonstrate that the flexibility, generality and efficiency of the proposed WaveletGLCA-GAN.
This work is funded by State Key Development Program (Grant No.2017YFC0821602, 2016YFB1001001, 2016YFB1001000) and the National Natural Science Foundation of China (Grants No. 61622310, 61427811, 61573360).
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