# Age and Gender Prediction using Deep CNNs and Transfer Learning

arXiv: https://arxiv.org/abs/2110.12633 · HTML: https://mlsanity.com/paper/2110.12633/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 25 Oct 2021.

## Builds on (most cited first, 2 of 2)

- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [On the Convergence of Adam and Beyond](https://mlsanity.com/paper/1904.09237/) (2019)

## Built on by (most cited first, 1 of 1)

- [P-Age: Pexels Dataset for Robust Spatio-Temporal Apparent Age Classification](https://mlsanity.com/paper/2311.02432/) (2023)

## Abstract (arXiv)

The last decade or two has witnessed a boom of images. With the increasing ubiquity of cameras and with the advent of selfies, the number of facial images available in the world has skyrocketed. Consequently, there has been a growing interest in automatic age and gender prediction of a person using facial images. We in this paper focus on this challenging problem. Specifically, this paper focuses on age estimation, age classification and gender classification from still facial images of an individual. We train different models for each problem and we also draw comparisons between building a custom CNN (Convolutional Neural Network) architecture and using various CNN architectures as feature extractors, namely VGG16 pre-trained on VGGFace, Res-Net50 and SE-ResNet50 pre-trained on VGGFace2 dataset and training over those extracted features. We also provide baseline performance of various machine learning algorithms on the feature extraction which gave us the best results. It was observed that even simple linear regression trained on such extracted features outperformed training CNN, ResNet50 and ResNeXt50 from scratch for age estimation.