  Images taken from the Internet have been used alongside Deep Learning for
many different tasks such as: smile detection, ethnicity, hair style, hair
colour, gender and age prediction. After witnessing these usages, we were
wondering what other attributes can be predicted from facial images available
on the Internet. In this paper we tackle the prediction of physical attributes
from face images using Convolutional Neural Networks trained on our dataset
named FIRW. We crawled around 61, 000 images from the web, then use face
detection to crop faces from these real world images. We choose ResNet-50 as
our base network architecture. This network was pretrained for the task of face
recognition by using the VGG-Face dataset, and we finetune it by using our own
dataset to predict physical attributes. Separate networks are trained for the
prediction of body type, ethnicity, gender, height and weight; our models
achieve the following accuracies for theses tasks, respectively: 84.58%,
87.34%, 97.97%, 70.51%, 63.99%. To validate our choice of ResNet-50 as the base
architecture, we also tackle the famous CelebA dataset. Our models achieve an
averagy accuracy of 91.19% on CelebA, which is comparable to state-of-the-art
approaches.
