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Attribute-Aware Face Aging With Wavelet-Based Generative Adversarial Networks
Open accessAbstract
Since it is difficult to collect face images of the same subject over a long range of age span, most existing face aging methods resort to unpaired datasets to learn age mappings. However, the matching ambiguity between young and aged face images inherent to unpaired training data may lead to unnatural changes of facial attributes during the aging process, which could not be solved by only enforcing identity consistency like most existing studies do. In this paper, we propose an attribute-aware face aging model with wavelet based Generative Adversarial Networks (GANs) to address the above issues. To be specific, we embed facial attribute vectors into both the generator and discriminator of the model to encourage each synthesized elderly face image to be faithful to the attribute of its corresponding input. In addition, a wavelet packet transform (WPT) module is incorporated to improve the visual fidelity of generated images by capturing age-related texture details at multiple scales in the frequency space. Qualitative results demonstrate the ability of our model in synthesizing visually plausible face images, and extensive quantitative evaluation results show that the proposed method achieves state-of-the-art performance on existing datasets.
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Citations
Cites
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Johnson, J., Alahi, A., & Fei-Fei, L. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_43 · Cited by 11,283 · cited 1×
“To be specific, we employ an hourglass-shaped fully convolutional network as the generator, which has achieved success in previous image translation studies [6, 29].”
MethodJoint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks
Zhang, K., Zhang, Z., Li, Z., & Qiao, Y. (2016) · IEEE Signal Processing Letters · doi:10.1109/lsp.2016.2603342 · Cited by 6,214 · cited 1×
“All face images are cropped and aligned according to the five facial landmarks detected by MTCNN [27].”
MethodParkhi, O. M., Vedaldi, A., & Zisserman, A. (2015) · Procedings of the British Machine Vision Conference 2015 · doi:10.5244/c.29.41 · Cited by 5,479 · cited 1×
“To be specific, we utilize the VGG-Face descriptor [14], denoted by φ, to extract the identity related semantic representation of a face image.”
Method“Least squares generative adversarial networks,”
Mao, X., Li, Q., Xie, H., Lau, R. Y. K., Wang, Z., & Smolley, S. P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.304 · Cited by 5,437 · cited 1×
“Unlike regular GANs [5], we adopt least square loss instead of negative log likelihood loss for that margins between generated samples and the decision boundary in the feature space are also minimized, which further improves the quality of synthesized images [12].”
MethodSelf-citationGenerative Adversarial Networks
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.2661 · Cited by 4,477 · cited 2×
“Unlike regular GANs [5], we adopt least square loss instead of negative log likelihood loss for that margins between generated samples and the decision boundary in the feature space are also minimized, which further improves the quality of synthesized images [12].”
Age Progression/Regression by Conditional Adversarial Autoencoder
Zhang, Z., Song, Y., & Qi, H. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.463 · Cited by 1,216 · cited 9×
“[28] proposed a conditional adversarial autoencoder (CAAE) to achieve age progression and regression by traversing in low-dimensional manifold.”
BackgroundMethodInfluentialToward Automatic Simulation of Aging Effects on Face Images
Lanitis, A., Taylor, C. J., & Cootes, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.993553 · Cited by 974 · cited 2×
“been proposed to address this problem in the last two decades [8, 20, 19, 21, 7].”
BackgroundPrototyping and Transforming Facial Textures for Perception Research
Tiddeman, B. P., Burt, M., & Perrett, D. (2001) · IEEE Computer Graphics and Applications · doi:10.1109/38.946630 · Cited by 838
MORPH: A Longitudinal Image Database of Normal Adult Age-Progression
Ricanek, K., & Tesafaye, T. (2006) · 7th International Conference on Automatic Face and Gesture Recognition (FGR06) · doi:10.1109/fgr.2006.78 · Cited by 778 · cited 3×
“Data samples in MORPH are color images of near-frontal faces exhibiting neutral expressions under uniform and moderate illumination with simple background.”
BackgroundInfluentialFace aging with conditional generative adversarial networks
Antipov, G., Baccouche, M., & Dugelay, J. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296650 · Cited by 597 · cited 1×
“To suppress such undesired changes of semantic information during the aging process, many recent face aging studies attempt to supervise the output by enforcing identity consistency [28, 1, 25, 9].”
BackgroundFace Recognition and Retrieval Using Cross-Age Reference Coding With Cross-Age Celebrity Dataset
Chen, B.-C., Chen, C.-S., & Hsu, W. H. (2015) · IEEE transactions on multimedia · doi:10.1109/tmm.2015.2420374 · Cited by 307 · cited 14×
“For CACD, since face images with race other than ‘white’ only takes a small portion of the entire dataset, we only select ‘gender’ as the attribute to preserve.”
BackgroundInfluentialA Compositional and Dynamic Model for Face Aging
Suo, J.-L., Zhu, S.-C., Shan, S., & Chen, X.-L. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.39 · Cited by 290 · cited 2×
“been proposed to address this problem in the last two decades [8, 20, 19, 21, 7].”
BackgroundMethodFace Aging with Identity-Preserved Conditional Generative Adversarial Networks
Tang, X., Wang, Z., Luo, W., & Gao, S. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00828 · Cited by 250 · cited 1×
“With the success of GANs [5] in generating visually appealing images, many efforts have been made to tackle the problem of face aging using GAN-based framework [28, 25, 9, 17, 24, 10].”
BackgroundThe perception of human growth.
Todd, J., Mark, L., Shaw, R., & Pittenger, J. (1980) · Scientific American · doi:10.1038/scientificamerican0280-132 · Cited by 245 · cited 1×
“[22] modeled the translation of facial appearance by revised cardioidal strain transformation.”
BackgroundIllumination-Aware Age Progression
Kemelmacher-Shlizerman, I., Suwajanakorn, S., & Seitz, S. M. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.426 · Cited by 224 · cited 2×
“been proposed to address this problem in the last two decades [8, 20, 19, 21, 7].”
BackgroundMethodWang, W., Cui, Z., Yan, Y., Feng, J., Yan, S., Shu, X., & Sebe, N. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.261 · Cited by 188 · cited 3×
“In recent years, deep generative models with temporal architectures are adopted to synthesize images of elderly faces [23, 3, 4].”
BackgroundLearning Face Age Progression: A Pyramid Architecture of GANsPreprint
Yang, H., Huang, D., Wang, Y.-H., & Jain, A. K. (2017) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.48550/arxiv.1711.10352 · Cited by 185 · cited 15×
“However, our method further generates face images of higher resolution (2×) with enhanced details compared to GLCA-GAN, and reduces ghosting artifacts in the results compared to PAG-GAN (e.g. finer details of hair and beard).”
BackgroundMethodResultInfluentialDeep Learning Face Attributes in the WildPreprint
Liu, Z., Luo, P., Wang, X., & Tang, X. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.7766 · Cited by 149 · cited 1×
“We choose ‘gender’ and ‘race’ to be the attributes that are required to be preserved, since these two attributes are guaranteed to remain unchanged during natural aging process, and are relatively objective compared to attributes such as ‘attractive’ or ‘chubby’ used in popular facial attribute dataset CelebA [11].”
BackgroundPersonalized Age Progression with Aging Dictionary
Shu, X.-B., Tang, J.-H., Lai, H.-J., Liu, L.-Q., & Yan, S. (2015) · IEEE International Conference on Computer Vision · doi:10.1109/iccv.2015.452 · Cited by 140 · cited 1×
“Face aging, also known as age progression [16], aims at rendering a given face image with aging effects while still preserving personalized features.”
BackgroundA Concatenational Graph Evolution Aging Model
Suo, J.-L., Chen, X.-L., Shan, S., Gao, W., & Dai, Q.-H. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.22 · Cited by 99 · cited 3×
“These four methods are (from left to right): CONGRE [18], HFA [26], GLCA-GAN [9], and PAG-GAN [25].”
MethodDual Conditional GANs for Face Aging and Rejuvenation
Song, J., Zhang, J., Gao, L., Liu, X., & Shen, H. T. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/125 · Cited by 77 · cited 1×
“With the success of GANs [5] in generating visually appealing images, many efforts have been made to tackle the problem of face aging using GAN-based framework [28, 25, 9, 17, 24, 10].”
BackgroundGlobal and Local Consistent Age Generative Adversarial Networks
Li, P., Hu, Y., Li, Q., He, R., & Sun, Z.-N. (2018) · International Conference on Pattern Recognition · doi:10.48550/arxiv.1801.08390 · Cited by 74 · cited 9×
“Following the convention in [25, 9], we divide the face images into four age groups, i.”
BackgroundMethodInfluentialSelf-citationTemporal Non-volume Preserving Approach to Facial Age-Progression and Age-Invariant Face Recognition
Duong, C. N., Quach, K. G., Luu, K., Le, T. H. N., & Savvides, M. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.403 · Cited by 65 · cited 2×
“In recent years, deep generative models with temporal architectures are adopted to synthesize images of elderly faces [23, 3, 4].”
BackgroundFace Aging Effect Simulation Using Hidden Factor Analysis Joint Sparse Representation
Yang, H., Huang, D., Wang, Y., Wang, H., & Tang, Y. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2547587 · Cited by 58 · cited 3×
“These four methods are (from left to right): CONGRE [18], HFA [26], GLCA-GAN [9], and PAG-GAN [25].”
MethodAge Progression and Regression with Spatial Attention Modules
Li, Q., Liu, Y.-F., & Sun, Z.-N. (2019) · AAAI Conference on Artificial Intelligence · doi:10.48550/arxiv.1903.02133 · Cited by 49 · cited 1×
“With the success of GANs [5] in generating visually appealing images, many efforts have been made to tackle the problem of face aging using GAN-based framework [28, 25, 9, 17, 24, 10].”
BackgroundSelf-citationFacial aging simulator considering geometry and patch-tiled texture
Tazoe, Y., Gohara, H., Maejima, A., & Morishima, S. (2012) · International Conference on Computer Graphics and Interactive Techniques · doi:10.1145/2342896.2343002 · Cited by 48 · cited 2×
“been proposed to address this problem in the last two decades [8, 20, 19, 21, 7].”
BackgroundGlobal and Local Consistent Wavelet-Domain Age Synthesis
Li, P., Hu, Y., He, R., & Sun, Z.-N. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.48550/arxiv.1809.07764 · Cited by 43 · cited 1×
“Note that this is different from [9], since wavelet coefficients are only used for discrimination in our work and no prediction or reconstruction is involved.”
BackgroundSelf-citationDuong, C., Quach, K. G., Luu, K., Le, T., Savvides, M., & Bui, T. D. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-019-01165-5 · Cited by 42 · cited 2×
“In recent years, deep generative models with temporal architectures are adopted to synthesize images of elderly faces [23, 3, 4].”
BackgroundGenerative Adversarial Nets for Multiple Text Corpora
Ebrahimi, A., Klabjan, D., & Wang, B. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9534025 · Cited by 1
Flagged first, then by citations
2 of the 32 references couldn’t be matched to a known paper.
Research works printed but not linked: 6
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- 10.1109/ICPR.2018.8545119 · DOI
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Cited by
Sorted by citations
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
Gui, J., Sun, Z., Wen, Y., Tao, D., & Ye, J. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3130191 · Cited by 1,231
“Face generation [332]–[340] is somewhat easy because there is only one class of object.”
BackgroundSelf-citationA Comprehensive Overview and Comparative Analysis on Deep Learning Models
Perumal, T., Mustapha, N., Mohamed, R., & Shiri, F. M. (2024) · Journal on artificial intelligence · doi:10.32604/jai.2024.054314 · Cited by 629
“Generative Adversarial Networks (GANs) have introduced numerous applications across various domains, including image blending [67], 3D object generation [68], face aging [69], medicine [70, 71], steganography [72], image manipulation [73], text transfer [74], language and speech synthesis [75], traffic control [76], a…”
BackgroundA Survey on Generative Adversarial Networks: Variants, Applications, and Training
Jabbar, A., Li, X., & Omar, B. (2021) · ACM Computing Surveys · doi:10.1145/3463475 · Cited by 406
“Face aging: Age-CGAN [49], CAAE [50], IP-CGAN [51], Wavelet-GANs [52, 53]”
MethodGenerative Adversarial Networks for Face Generation: A Survey
Kammoun, A., Slama, R., Tabia, H., Ouni, T., & Abid, M. (2022) · ACM Computing Surveys · doi:10.1145/3527850 · Cited by 146
“Liu et al. [89] introduced an attribute-aware face aging model with wavelet-based GANs for aged face image synthesis.”
MethodWavelet Integrated CNNs for Noise-Robust Image ClassificationPreprint
Li, Q., Shen, L., Guo, S., & Lai, Z.-H. (2020) · Computer Vision and Pattern Recognition · doi:10.48550/arxiv.2005.03337 · Cited by 268
Self-citation
Metadata
- Authors
- Liu, Y., Li, Q., & Sun, Z.
- Affiliations
- Chinese Academy of Sciences (CN)
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 11869-11878
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2964261768
- Cited by
- 156
- References
- 32
- Citations by year
- 2026: 2 · 2025: 11 · 2024: 19 · 2023: 21 · 2022: 28 · 2021: 48 · 2020: 24 · 2019: 3
- FWCI
- 8.39
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61427811, 61702513, U1836217)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- facial aging, generative adversarial networks, wavelet packet decomposition, facial attributes, unpaired training data, identity consistency
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