Age and Gender Prediction using Deep CNNs and
Transfer Learning
Vikas Sheoran
1
,
Shreyansh Joshi
2
and Tanisha R. Bhayani
3
1
Birla Institute of Technology & Science, Pilani - Hyderabad campus,
Hyderabad 500078, India.
f20180847@hyderabad.bits-pilani.ac.in
2
Birla Institute of Technology & Science, Pilani - Goa campus,
Goa 403726, India.
f20180097@goa.bits-pilani.ac.in
3
Silver Touch Technologies Limited, Ahmedabad, 380006, India.
t.bhayani@yahoo.com.
Abstract
. 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 availa-
ble 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 build-
ing 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 ex-
tracted 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 train-
ing CNN, ResNet50 and ResNeXt50 from scratch for age estimation.
Keywords: Age Estimation, CNN, Transfer Learning.
