# C3AE: Exploring the Limits of Compact Model for Age Estimation

arXiv: https://arxiv.org/abs/1904.05059 · HTML: https://mlsanity.com/paper/1904.05059/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2019.

## Built on by (most cited first, 26 of 26)

- [Moving Window Regression: A Novel Approach to Ordinal Regression](https://mlsanity.com/paper/2203.13122/) (2022)
- [PML: Progressive Margin Loss for Long-tailed Age Classification](https://mlsanity.com/paper/2103.02140/) (2021)
- [Masked Contrastive Graph Representation Learning for Age Estimation](https://mlsanity.com/paper/2306.17798/) (2023)
- [FP-Age: Leveraging Face Parsing Attention for Facial Age Estimation in the Wild](https://mlsanity.com/paper/2106.11145/) (2021)
- [Auto-Encoding Score Distribution Regression for Action Quality Assessment](https://mlsanity.com/paper/2111.11029/) (2021)
- [Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender Estimation](https://mlsanity.com/paper/2403.02302/) (2024)
- [TAA-GCN: A Temporally Aware Adaptive Graph Convolutional Network for Age Estimation](https://mlsanity.com/paper/2305.08779/) (2023)
- [A Multi-view Mask Contrastive Learning Graph Convolutional Neural Network for Age Estimation](https://mlsanity.com/paper/2407.16234/) (2024)
- [Generation of Non-Deterministic Synthetic Face Datasets Guided by Identity Priors](https://mlsanity.com/paper/2112.03632/) (2021)
- [Child Face Recognition at Scale: Synthetic Data Generation and Performance Benchmark](https://mlsanity.com/paper/2304.11685/) (2023)
- [DAA: A Delta Age AdaIN operation for age estimation via binary code transformer](https://mlsanity.com/paper/2303.07929/) (2023)
- [MetaAge: Meta-Learning Personalized Age Estimators](https://mlsanity.com/paper/2207.05288/) (2022)
- [Time flies by: Analyzing the Impact of Face Ageing on the Recognition Performance with Synthetic Data](https://mlsanity.com/paper/2208.08207/) (2022)
- [GroupFace: Imbalanced Age Estimation Based on Multi-hop Attention Graph Convolutional Network and Group-aware Margin Optimization](https://mlsanity.com/paper/2412.11450/) (2024)
- [ComFace: Facial Representation Learning with Synthetic Data for Comparing Faces](https://mlsanity.com/paper/2405.16016/) (2024)
- [MobileAgeNet: Lightweight Facial Age Estimation for Mobile Deployment](https://mlsanity.com/paper/2604.17007/) (2026)
- [Using Robust Regression to Find Font Usage Trends](https://mlsanity.com/paper/2106.15232/) (2021)
- [A Survey of Face Recognition](https://mlsanity.com/paper/2212.13038/) (2022)
- [P-Age: Pexels Dataset for Robust Spatio-Temporal Apparent Age Classification](https://mlsanity.com/paper/2311.02432/) (2023)
- [LRA-GNN: Latent Relation-Aware Graph Neural Network with Initial and Dynamic Residual for Facial Age Estimation](https://mlsanity.com/paper/2502.05423/) (2025)
- [Stage-wise Adaptive Label Distribution for Facial Age Estimation](https://mlsanity.com/paper/2509.00450/) (2025)
- [Age Prediction From Face Images Via Contrastive Learning](https://mlsanity.com/paper/2308.11896/) (2023)
- [The Age-specific Alzheimer 's Disease Prediction with Characteristic Constraints in Nonuniform Time Span](https://mlsanity.com/paper/2511.21530/) (2025)
- [Can a Teenager Fool an AI? Evaluating Low-Cost Cosmetic Attacks on Age Estimation Systems](https://mlsanity.com/paper/2602.19539/) (2026)
- [U-Face: An Efficient and Generalizable Framework for Unsupervised Facial Attribute Editing via Subspace Learning](https://mlsanity.com/paper/2603.14004/) (2026)
- [Artificial Intelligence as a Tool for Combating Child Labour: A Real-Time Edge Vision Pipeline for Child Detection and Age Estimation](https://mlsanity.com/paper/2608.14770/) (2026)

## Abstract (arXiv)

Age estimation is a classic learning problem in computer vision. Many larger and deeper CNNs have been proposed with promising performance, such as AlexNet, VggNet, GoogLeNet and ResNet. However, these models are not practical for the embedded/mobile devices. Recently, MobileNets and ShuffleNets have been proposed to reduce the number of parameters, yielding lightweight models. However, their representation has been weakened because of the adoption of depth-wise separable convolution. In this work, we investigate the limits of compact model for small-scale image and propose an extremely Compact yet efficient Cascade Context-based Age Estimation model(C3AE). This model possesses only 1/9 and 1/2000 parameters compared with MobileNets/ShuffleNets and VggNet, while achieves competitive performance. In particular, we re-define age estimation problem by two-points representation, which is implemented by a cascade model. Moreover, to fully utilize the facial context information, multi-branch CNN network is proposed to aggregate multi-scale context. Experiments are carried out on three age estimation datasets. The state-of-the-art performance on compact model has been achieved with a relatively large margin.