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Attribute-aware Face Aging with Wavelet-based Generative Adversarial Networks
Yunfan Liu, Qi Li, Zhenan Sun
TL;DR
Because same-subject face images across long age spans are difficult to collect, face aging often uses unpaired data, where matching ambiguity can produce unnatural attribute changes that identity consistency alone does not prevent. The paper conditions a GAN on facial attributes and uses wavelet packet features for multiscale aging textures; it reports state-of-the-art quantitative performance and visually plausible aging results.
Problem
Long-span same-subject face images are difficult to collect, so unpaired training is common, but matching ambiguity can cause unnatural attribute changes that identity consistency alone does not resolve.
Source: §1 · pp. 1–2Method
A GAN embeds facial attribute vectors in both generator and discriminator and uses wavelet packet transform to capture multiscale frequency-domain aging textures.
Source: §1 · p. 2Results
Takeaways & Limitations
Abstract
from arXiv · showhide
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 a 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 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 to synthesize visually plausible face images, and extensive quantitative evaluation results show that the proposed method achieves state-of-the-art performance on existing datasets.
1. Introduction
Face aging seeks to add aging effects while preserving personalized features, but collecting long-span images of the same person is difficult. Unpaired training can confuse aging with attribute changes, and identity consistency alone does not resolve that ambiguity; the paper proposes attribute-aware GANs with wavelet-based texture features.
1. Introduction
- Collecting same-subject face images across a long age span is difficult, limiting the practical applicability of methods that require them.The paper notes that many recent studies therefore use unpaired face-aging data.Source: §1 · p. 1
- Unpaired training pairs can have mismatched attributes, misleading models into non-aging translations, ghosting artifacts, and incorrect facial attributes.The example of a young woman paired with an old man can lead to a generated beard on the woman.Source: §1 · pp. 1–2
- Identity consistency alone is insufficient: personalized features may remain while facial attributes change unnaturally in unpaired face aging.The paper identifies matching ambiguity in unpaired data as the reason identity preservation does not ensure reasonable aging results.Source: §1 · p. 2
- The proposed GAN embeds facial attribute vectors in both generator and discriminator to keep generated elderly faces faithful to each input's attributes.This conditioning is intended to regulate the aging process beyond identity preservation.Source: §1 · p. 2
- Wavelet packet transform extracts multiscale frequency-domain texture features to generate fine-grained aging details.The paper treats wrinkles, laugh lines, and eye bags as local textures associated with aging.Source: §1 · p. 2
- The authors report state-of-the-art quantitative performance and experiments demonstrating accurate aging effects alongside preservation of identity and facial attributes.The abstract contribution list states the overall benchmark claim, while the experiments are described as evaluating aging accuracy and preservation.Source: §1 · p. 2
2. Related Work
Related work spans physical models, prototype-based methods, and deep learning. The paper distinguishes its GAN approach by addressing attribute consistency under unpaired training, beyond the identity preservation emphasized in prior GAN work.
2. Related Work
- Physical models simulate facial change over time from anatomy, but are computationally expensive and require many same-subject image sequences.Examples model cardioidal strain and facial muscles or overall structure.Source: §2 · p. 2
- Prototype methods average faces into age-group representatives and infer aging from translations between prototypes, but averaging eliminates personalized features.Source: §2 · p. 2
- Deep generative aging methods can require long-span same-subject sequences, limiting practical use; GANs have also been applied to face aging [10].CAAE uses low-dimensional manifold traversal for age progression and regression; uses a pyramid GAN and identity loss.Source: §2 · p. 2
- Unlike prior work emphasizing identity preservation, this paper targets matching ambiguity in unpaired samples by embedding facial attributes to maintain attribute consistency.The section identifies the pyramid-architecture GAN as the work most similar to its approach.Source: §2 · p. 2
3. Approach
The approach conditions face aging on input facial attributes while using wavelet-based discrimination to capture age-related textures. It combines these components with adversarial, pixel, and identity objectives.
3. Approach
- The model takes young face images and their attribute vectors as input, then generates visually plausible aged faces intended to preserve those attributes.Its generator and discriminator both use attribute information to reduce ambiguity in unpaired mappings.Source: §3 · pp. 2–3
3.1. Facial Attribute Embedded Generator
- The generator embeds facial attributes in its bottleneck and uses a residual connection to focus on aging differences rather than reconstructing the whole face.An hourglass encoder-decoder with four residual blocks forms the generator; the input image is added to the decoder output.Source: §3.1 · p. 3
3.2. Wavelet-based Discriminator
- The discriminator judges realism and attribute consistency using multi-level wavelet coefficients that capture local aging textures at multiple scales.Attribute vectors are concatenated into each pathway, whose outputs are fused; WPT replaces a sequence of convolutional layers for multi-scale analysis with fewer convolutions.Source: §3.2 · pp. 3–4
3.3. Overall Objective Functions
- Training alternates generator and discriminator optimization, with pixel and identity losses supplementing least-squares adversarial losses.Only aged faces with attributes matching the input are positive samples, while real young faces are negative samples; pixel and identity terms maintain image-level and personalized-feature consistency.Source: §3.3 · p. 4
4. Experiments
Experiments on Morph and CACD assess visual aging quality, age accuracy, identity preservation, facial-attribute consistency, and the effects of attribute embedding and wavelet packet transforms. Results favor the proposed method, while CACD’s noisy labels qualify several comparisons.
4.1. Dataset
- MORPH contains 55,000 images from more than 13,000 subjects; CACD has 163,446 celebrity images and label mismatches, alongside less-controlled pose, illumination, and expression.MORPH uses gender and race attributes, while CACD uses gender because other races form a small portion of that dataset.Source: §4.1 · pp. 4–5
4.3. Qualitative Results of Face Aging
- Visual samples show plausible aging across Morph and CACD, robust to race, gender, expression, and occlusion; compared with GLCA-GAN, outputs have 2× resolution and enhanced details.The method also reduces ghosting relative to PAG-GAN; traditional CONGRE and HFA produce only subtle aging effects.Source: Figure 4 · p. 5, §4.3 · pp. 5–6, Figure 5 · p. 5
4.2. Implementation Details
- Experiments translate faces from age 30 or younger into three older groups and use public Face++ APIs for objective measurements and comparable verification thresholds.The study uses five-fold cross-validation; training uses Adam with a 1e−4 learning rate and batch size 16.Source: §4.2 · p. 5
4.4. Aging Accuracy and Identity Preservation
- On Morph, the method consistently outperforms prior approaches in aging accuracy and verification; on noisy-label CACD, it remains comparable to prior state-of-the-art.Verification confidence and accuracy decrease as the time interval between a subject’s images increases.Source: Table 1 · p. 6, §4.4 · pp. 6–7, Table 2 · p. 7
4.5. Facial Attribute Consistency
- On Morph, preservation reaches up to 97.37% for gender and 95.86% for race; CACD gender preservation reaches 87.19% versus 70.05% for 51+.The passage reports a 17.14% advantage for the oldest age group and notes that preservation performance drops on CACD amid mislabeled samples.Source: Table 3 · p. 7, §4.5 · p. 7
4.6. Ablation Study
- Facial attribute embedding increases gender and race preservation across all three age mappings, especially translation to 51+, reducing unpaired-data mapping ambiguity.The ablation examines variants on Morph because CACD labels are noisy.Source: §4.6 · pp. 7–8, Table 4 · p. 8
- Wavelet packet transform reduces age-distribution discrepancies and alleviates ghosting, but provides little help with attribute consistency because it does not bridge the semantic gap.Visual ablations compare variants with and without FAE and WPT.Source: Figure 7 · p. 7, §4.6 · p. 8, Table 4 · p. 8
5. Conclusion
The conclusion presents a GAN framework that uses facial attributes to address ambiguity in unpaired face aging, with wavelet packet features for visual fidelity. Experiments on Morph and CACD support its aging accuracy and identity and attribute preservation.
5. Conclusion
- The framework embeds facial attribute vectors in both generator and discriminator to keep synthesized faces faithful to input attributes, while WPT extracts multiscale texture features.The paper reports qualitative robustness to PIE variations and noisy labels, and quantitative effectiveness in aging accuracy and identity and attribute preservation.Source: §5 · p. 8
- The method targets matching ambiguity in unpaired aging data, which identity constraints alone do not resolve.The experiments are conducted on Morph and CACD.Source: §5 · p. 8
- The paper concludes that its experiments support lifelike aged-face synthesis and preservation of identity and facial attributes.Source: §5 · p. 8