# Deep Regression Forests for Age Estimation

arXiv: https://arxiv.org/abs/1712.07195 · HTML: https://mlsanity.com/paper/1712.07195/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2017.

## Built on by (most cited first, 30 of 35)

- [Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression](https://mlsanity.com/paper/2103.13629/) (2021)
- [Moving Window Regression: A Novel Approach to Ordinal Regression](https://mlsanity.com/paper/2203.13122/) (2022)
- [OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression](https://mlsanity.com/paper/2206.02338/) (2022)
- [SwinFace: A Multi-task Transformer for Face Recognition, Expression Recognition, Age Estimation and Attribute Estimation](https://mlsanity.com/paper/2308.11509/) (2023)
- [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)
- [Unimodal-Concentrated Loss: Fully Adaptive Label Distribution Learning for Ordinal Regression](https://mlsanity.com/paper/2204.00309/) (2022)
- [Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification](https://mlsanity.com/paper/2306.13856/) (2023)
- [FP-Age: Leveraging Face Parsing Attention for Facial Age Estimation in the Wild](https://mlsanity.com/paper/2106.11145/) (2021)
- [CILF-CIAE: CLIP-driven Image-Language Fusion for Correcting Inverse Age Estimation](https://mlsanity.com/paper/2312.01758/) (2023)
- [Improving Face-Based Age Estimation with Attention-Based Dynamic Patch Fusion](https://mlsanity.com/paper/2112.10167/) (2021)
- [Faceptor: A Generalist Model for Face Perception](https://mlsanity.com/paper/2403.09500/) (2024)
- [Hierarchical Attention-based Age Estimation and Bias Estimation](https://mlsanity.com/paper/2103.09882/) (2021)
- [Meta Ordinal Regression Forest for Medical Image Classification with Ordinal Labels](https://mlsanity.com/paper/2203.07725/) (2022)
- [A Survey of Neural Trees](https://mlsanity.com/paper/2209.03415/) (2022)
- [TAA-GCN: A Temporally Aware Adaptive Graph Convolutional Network for Age Estimation](https://mlsanity.com/paper/2305.08779/) (2023)
- [Adaptive Mean-Residue Loss for Robust Facial Age Estimation](https://mlsanity.com/paper/2203.17156/) (2022)
- [MetaAge: Meta-Learning Personalized Age Estimators](https://mlsanity.com/paper/2207.05288/) (2022)
- [Adversarial Regression Learning for Bone Age Estimation](https://mlsanity.com/paper/2103.06149/) (2021)
- [Lipschitzness Effect of a Loss Function on Generalization Performance of Deep Neural Networks Trained by Adam and AdamW Optimizers](https://mlsanity.com/paper/2303.16464/) (2023)
- [General vs. Long-Tailed Age Estimation: An Approach to Kill Two Birds with One Stone](https://mlsanity.com/paper/2307.10129/) (2023)
- [MobileAgeNet: Lightweight Facial Age Estimation for Mobile Deployment](https://mlsanity.com/paper/2604.17007/) (2026)
- [Shrub Ensembles for Online Classification](https://mlsanity.com/paper/2112.03723/) (2021)
- [Transition Matrix Representation of Trees with Transposed Convolutions](https://mlsanity.com/paper/2202.10677/) (2022)
- [Efficient Normalized Conformal Prediction and Uncertainty Quantification for Anti-Cancer Drug Sensitivity Prediction with Deep Regression Forests](https://mlsanity.com/paper/2402.14080/) (2024)
- [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)
- [Anny-Fit: All-Age Human Mesh Recovery](https://mlsanity.com/paper/2605.04728/) (2026)
- [Making CNNs Interpretable by Building Dynamic Sequential Decision Forests with Top-down Hierarchy Learning](https://mlsanity.com/paper/2106.02824/) (2021)
- [Self-Paced Deep Regression Forests with Consideration of Ranking Fairness](https://mlsanity.com/paper/2112.06455/) (2021)

## Abstract (arXiv)

Age estimation from facial images is typically cast as a nonlinear regression problem. The main challenge of this problem is the facial feature space w.r.t. ages is heterogeneous, due to the large variation in facial appearance across different persons of the same age and the non-stationary property of aging patterns. In this paper, we propose Deep Regression Forests (DRFs), an end-to-end model, for age estimation. DRFs connect the split nodes to a fully connected layer of a convolutional neural network (CNN) and deal with heterogeneous data by jointly learning input-dependant data partitions at the split nodes and data abstractions at the leaf nodes. This joint learning follows an alternating strategy: First, by fixing the leaf nodes, the split nodes as well as the CNN parameters are optimized by Back-propagation; Then, by fixing the split nodes, the leaf nodes are optimized by iterating a step-size free and fast-converging update rule derived from Variational Bounding. We verify the proposed DRFs on three standard age estimation benchmarks and achieve state-of-the-art results on all of them.