  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.
