  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.
