# Transfer Learning with Deep CNNs for Gender Recognition and Age Estimation

arXiv: https://arxiv.org/abs/1811.07344 · HTML: https://mlsanity.com/paper/1811.07344/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [Deep Generative Views to Mitigate Gender Classification Bias Across Gender-Race Groups](https://mlsanity.com/paper/2208.08382/) (2022)
- [Facial Age Estimation using Convolutional Neural Networks](https://mlsanity.com/paper/2105.06746/) (2021)
- [Data Provenance via Differential Auditing](https://mlsanity.com/paper/2209.01538/) (2022)

## Abstract (arXiv)

In this project, competition-winning deep neural networks with pretrained weights are used for image-based gender recognition and age estimation. Transfer learning is explored using both VGG19 and VGGFace pretrained models by testing the effects of changes in various design schemes and training parameters in order to improve prediction accuracy. Training techniques such as input standardization, data augmentation, and label distribution age encoding are compared. Finally, a hierarchy of deep CNNs is tested that first classifies subjects by gender, and then uses separate male and female age models to predict age. A gender recognition accuracy of 98.7% and an MAE of 4.1 years is achieved. This paper shows that, with proper training techniques, good results can be obtained by retasking existing convolutional filters towards a new purpose.