全局–局部协同嵌入与语义掩码驱动的年龄化方法

刘耀晖; 刘佳鑫; 孙鹏; 沈喆; 郎宇博

doi:10.11999/JEIT250430

全局–局部协同嵌入与语义掩码驱动的年龄化方法

doi: 10.11999/JEIT250430 cstr: 32379.14.JEIT250430

刘耀晖¹,
刘佳鑫¹,
孙鹏^{1, 2, ,},
沈喆³,
郎宇博¹

1.
中国刑事警察学院公安信息技术与情报学院沈阳 110854
2.
司法部司法鉴定重点实验室上海 200063
3.
沈阳航空航天大学民航学院沈阳 110135

基金项目: 国家自然科学基金(61307016)，辽宁省教育厅科技创新团队 (LJ222410175007)，辽宁省研究生教育教学改革研究 (LNYJG2023317)，沈阳市科技计划项目社会治理科技专项(24-213-3-41)

详细信息

作者简介:
刘耀晖：男，硕士生，研究方向为人像增龄

刘佳鑫：男，硕士生，研究方向为人像模拟

孙鹏：男，教授，博士生导师，研究方向为视频侦查、图像处理、多媒体取证

沈喆：女，讲师，硕士生导师，研究方向为图像处理

郎宇博：男，讲师，研究方向为图像处理、视频图像伪造篡改检验

通讯作者:
孙鹏　6094079@qq.com

中图分类号: TP391.41
计量
- 文章访问数: 49
- HTML全文浏览量: 15
- PDF下载量: 8
- 被引次数: 0
出版历程
- 收稿日期: 2025-05-19
- 修回日期: 2025-08-16
- 网络出版日期: 2025-08-29

Global–local Co-embedding and Semantic Mask-driven Aging Approach

LIU Yaohui¹,
LIU Jiaxin¹,
SUN Peng^{1, 2
, ,},
SHEN Zhe³,
LANG Yubo¹

1.
School of Public Security Information Technology and Intelligence, Criminal Investigation Police University of China, Shenyang 110854, China
2.
Key Lab of Forensic Science, Ministry of Justice, China (Academy of Forensic Science), Shanghai 200063, China
3.
Civil Aviation College, Shenyang Aerospace University, Shenyang 110135, China

Funds: The National Natural Science Foundation of China (61307016), The Science and Technology Innovation Team Project of the Department of Education of Liaoning Province (LJ222410175007), Liaoning Province Graduate Education and Teaching Reform Research Funded Project (LNYJG2023317), Shenyang Municipal Science and Technology Program Project on Social Governance Technology (24-213-3-41)

摘要

摘要: 人像年龄化要求在保留输入人像个体特征与身份信息的同时生成指定年龄人像。针对现有方法在嵌入阶段存在的特征解耦能力不足，头发、眼镜等非年龄化因素对皮肤纹理建模干扰产生伪影的问题，该文提出一种全局–局部协同嵌入与语义掩码驱动的年龄化方法(GLS-Age)。通过全局–局部协同嵌入策略对不同潜在空间分配差异化的学习任务，在保持人像全局一致性的同时，增强了对睫毛、皮肤纹理等局部细节的还原能力，显著改善了嵌入人像的感知质量；针对非年龄化因素对皮肤纹理建模的干扰，设计一种语义掩码驱动的非年龄化区域编辑模块，通过图像填充技术对输入人像进行重构去除非年龄化因素，从而避免在年龄化过程中引入伪影。为精确迁移输入人像中头发、眼镜等非年龄化要素，进一步构建可微生成器DsGAN实现迁移潜码与原始嵌入潜码的高效对齐，确保生成人像在语义与结构上的一致性。在CACD、CelebA等公开基准数据集上的实验结果表明，GLS-Age在确保年龄转化效果的同时显著提升了身份一致性。同时在Face++平台评估中，GLS-Age所生成人像在身份置信度和年龄预测分布等指标上均获得了优异的评分。
- 人像年龄化 /
- 全局–局部协同嵌入 /
- 非年龄化因素 /
- 语义掩码驱动 /
- 身份置信度
Abstract: Objective Facial age progression has become increasingly important in applications such as criminal investigation and digital identity authentication, making it a key research area in computer vision. However, existing mainstream facial age progression networks face two primary limitations. First, they tend to overemphasize the embedding of age-related features, often at the expense of preserving identity-consistent multi-scale attributes. Second, they fail to effectively eliminate interference from non-age-related elements such as hair and glasses, leading to suboptimal performance in complex scenarios. To address these challenges, this study proposes a global–local co-embedding and semantic mask-driven aging method. The global–local co-embedding strategy improves the accuracy of input portrait reconstruction while reducing computational cost during the embedding phase. In parallel, a semantic mask editing mechanism is introduced to remove non-age-related features—such as hair and eyewear—thereby enabling more accurate embedding of age-related characteristics. This dual strategy markedly enhances the model’s capacity to learn and represent age-specific attributes in facial imagery. Methods A Global–Local Collaborative Embedding (GLCE) strategy is proposed to achieve high-quality latent space mapping of facial images. Distinct learning objectives are assigned to separate latent subspaces, which enhances the representation of fine-grained facial features while preserving identity-specific information. Therefore, identity consistency is improved, and both training time and computational cost are reduced, increasing the efficiency of feature extraction. To address interference from non-age-related elements, a semantic mask-driven editing mechanism is employed. Semantic segmentation and image inpainting techniques are integrated to accurately remove regions such as hair and glasses that hinder precise age modeling. A differentiable generator, DsGAN, is introduced to align the transferred latent codes with the embedded identity-preserving codes. Through this alignment, the expression of age-related features is enhanced, and identity information is better retained during the age progression process. Results and Discussions Experimental results on benchmark datasets, including CCAD and CelebA, demonstrate that GLS-Age outperforms existing methods such as IPCGAN, CUSP, SAM, and LATS in identity confidence assessment. The age distributions of the generated portraits are also more closely aligned with those of the target age groups. Qualitative analysis further shows that, in cases with hair occlusion, GLS-Age produces more realistic wrinkle textures and enables more accurate embedding of age-related features compared with other methods. Simultaneously, it significantly improves the identity consistency of the synthesized facial images. Conclusions This study addresses core challenges in facial age progression, including identity preservation, inadequate detail modeling, and interference from non-age-related factors. A novel Global–Local collaborative embedding and Semantic mask-driven Aging method (GLS-Age) is proposed to resolve these limitations. By employing a differentiated latent space learning strategy, the model achieves hierarchical decoupling of structural and textural features. When integrated with semantic-guided portrait editing and a differentiable generator for latent space alignment, GLS-Age markedly enhances both the fidelity of age feature expression and the consistency of identity retention. The method demonstrates superior generalization and synthesis quality across multiple benchmark datasets, effectively reproducing natural wrinkle patterns and age-related facial changes. These results confirm the feasibility and advancement of GLS-Age in facial age synthesis tasks. Furthermore, this study establishes a compact, high-quality dataset focused on Asian facial portraits, supporting further research in image editing and face generation within this demographic. The proposed method not only contributes technical support to practical applications such as cold case resolution and missing person identification in public security but also offers a robust data and modeling framework for advancing human age-based simulation technologies. Future work will focus on enhancing controllable editing within latent spaces, improving anatomical plausibility in skull structure transformations, and strengthening model performance across extreme age groups, including infants and the elderly. These efforts aim to expand the application of facial age progression in areas such as forensic analysis, humanitarian family search, and social security systems.
- Age-oriented /
- Global–Local Collaborative Embedding (GLCE) /
- Non-age-related factors /
- Semantic mask-driven /
- Identity confidence

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图 1 GLS-Age网络架构图

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图 2 非年龄化区域迁移模块

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图 3 GLS-Age与CUSP, LATS对比示意图

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图 4 GLS-Age与HRFAE, IPCGAN对比示意图

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图 5 GLS-Age与FADING, SAM对比示意图

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图 6 不同模块消融对比

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表 1 测试集上生成人像的预测年龄分布

	模型	年龄估计值(岁)
	模型	0	10	20	30	50
Eur	CUSP	7.42±2.44	14.57±2.97	26.61±6.09	36.32±8.01	57.12±14.01
	LATS	8.11±3.25	13.76±2.09	26.54±5.33	37.87±7.43	59.03±12.54
	HRFAE	19.41±15.34	24.56±6.39	24.90±7.13	25.53±8.19	34.12±12.98
	IPCGAN	-	-	27.31±6.67	35.76±8.11	56.78±17.21
	SAM	9.84±5.66	21.68±3.05	23.67±2.46	34.58±4.61	56.96±7.78
	FADING	12.09±2.27	12.31±4.27	24.72±6.13	34.33±3.63	59.13±5.43
	本文	7.01±2.01	15.22±3.04	23.33±2.74	33.29±3.17	56.25±7.10
As	CUSP	7.11±2.91	11.35±4.13	24.19±4.87	36.96±7.71	58.76±12.22
	LATS	7.11±3.56	12.23±4.54	24.31±4.56	35.67±6.93	57.83±13.10
	HRFAE	17.93±10.43	22.21±5.33	27.42±7.54	24.90±6.33	30.10±8.12
	IPCGAN	-	-	28.83±6.11	34.98±7.86	58.21±13.72
	SAM	10.55±4.33	19.72±5.44	24.15±3.42	33.21±4.26	57.69±8.37
	FADING	11.44±2.91	12.21±5.79	25.12±5.19	34.27±3.74	57.12±6.16
	本文	6.53±2.88	13.59±3.09	23.97±3.42	34.15±3.01	55.65±7.44

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表 2 测试集上生成人像的身份置信度

	模型	身份置信度(%)
	模型	0	10	20	30	50
Eur	CUSP	61.01	68.46	85.44	98.12	89.12
	LATS	65.11	73.23	97.33	98.06	94.21
	HRFAE	94.51	96.38	97.47	98.81	97.12
	IPCGAN	-	-	96.42	95.35	96.79
	SAM	72.16	85.08	98.43	98.54	96.14
	FADING	54.47	79.41	89.19	97.23	94.77
	本文	75.54	87.79	98.59	98.81	97.19
As	CUSP	60.13	65.13	84.31	97.59	86.67
	LATS	60.56	77.12	88.56	85.62	81.92
	HRFAE	96.67	98.11	95.37	96.29	97.37
	IPCGAN	-	-	94.95	97.54	97.33
	SAM	69.45	72.68	87.55	81.61	81.44
	FADING	56.21	79.55	90.05	98.33	95.48
	OURS	70.93	87.24	98.45	98.17	97.21

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表 3 消融各模块定量比对

模型/测试年龄组	年龄估计值(岁)	身份置信度(%)
Real face	42.52±4.27	/
GLS-Age	41.17±6.32	98.22
w/o GLCE	37.51±8.13	65.01
w/o SM-NAE	47.09±5.53	95.67
with FS	/	99.92
with Restyle	/	97.76

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