# Age Estimation

74 papers with code • 16 benchmarks • 19 datasets

Age Estimation is the task of estimating the age of a person from an image some other kind of data.

( Image credit: BridgeNet )

## Libraries

Use these libraries to find Age Estimation models and implementations## Most implemented papers

# Rank consistent ordinal regression for neural networks with application to age estimation

In many real-world prediction tasks, class labels include information about the relative ordering between labels, which is not captured by commonly-used loss functions such as multi-category cross-entropy.

# Deep Label Distribution Learning with Label Ambiguity

However, it is difficult to collect sufficient training images with precise labels in some domains such as apparent age estimation, head pose estimation, multi-label classification and semantic segmentation.

# Face Aging With Conditional Generative Adversarial Networks

It has been recently shown that Generative Adversarial Networks (GANs) can produce synthetic images of exceptional visual fidelity.

# Deep Regression Forests for Age Estimation

Age estimation from facial images is typically cast as a nonlinear regression problem.

# On the effect of age perception biases for real age regression

Our model outperformed a state-of-the-art architecture proposed to separately address apparent and real age regression.

# Facial age estimation by deep residual decision making

Residual representation learning simplifies the optimization problem of learning complex functions and has been widely used by traditional convolutional neural networks.

# Fair and accurate age prediction using distribution aware data curation and augmentation

One of the main causes of unfair behavior in age prediction methods lies in the distribution and diversity of the training data.

# PFA-GAN: Progressive Face Aging with Generative Adversarial Network

Although impressive results have been achieved with conditional generative adversarial networks (cGANs), the existing cGANs-based methods typically use a single network to learn various aging effects between any two different age groups.

# Improved Brain Age Estimation with Slice-based Set Networks

Deep Learning for neuroimaging data is a promising but challenging direction.

# OTFPF: Optimal Transport-Based Feature Pyramid Fusion Network for Brain Age Estimation with 3D Overlapped ConvNeXt

In this paper, we propose an end-to-end neural network architecture, referred to as optimal transport based feature pyramid fusion (OTFPF) network, for the brain age estimation with T1 MRIs.