  Age and gender are complementary soft biometric traits for face recognition.
Successful estimation of age and gender from facial images taken under
real-world conditions can contribute improving the identification results in
the wild. In this study, in order to achieve robust age and gender
classification in the wild, we have benefited from Deep Convolutional Neural
Networks based representation. We have explored transferability of existing
deep convolutional neural network (CNN) models for age and gender
classification. The generic AlexNet-like architecture and domain specific
VGG-Face CNN model are employed and fine-tuned with the Adience dataset
prepared for age and gender classification in uncontrolled environments. In
addition, task specific GilNet CNN model has also been utilized and used as a
baseline method in order to compare with transferred models. Experimental
results show that both transferred deep CNN models outperform the GilNet CNN
model, which is the state-of-the-art age and gender classification approach on
the Adience dataset, by an absolute increase of 7% and 4.5% in accuracy,
respectively. This outcome indicates that transferring a deep CNN model can
provide better classification performance than a task specific CNN model, which
has a limited number of layers and trained from scratch using a limited amount
of data as in the case of GilNet. Domain specific VGG-Face CNN model has been
found to be more useful and provided better performance for both age and gender
classification tasks, when compared with generic AlexNet-like model, which
shows that transfering from a closer domain is more useful.
