  We address the use of selfie ocular images captured with smartphones to
estimate age and gender. Partial face occlusion has become an issue due to the
mandatory use of face masks. Also, the use of mobile devices has exploded, with
the pandemic further accelerating the migration to digital services. However,
state-of-the-art solutions in related tasks such as identity or expression
recognition employ large Convolutional Neural Networks, whose use in mobile
devices is infeasible due to hardware limitations and size restrictions of
downloadable applications. To counteract this, we adapt two existing
lightweight CNNs proposed in the context of the ImageNet Challenge, and two
additional architectures proposed for mobile face recognition. Since datasets
for soft-biometrics prediction using selfie images are limited, we counteract
over-fitting by using networks pre-trained on ImageNet. Furthermore, some
networks are further pre-trained for face recognition, for which very large
training databases are available. Since both tasks employ similar input data,
we hypothesize that such strategy can be beneficial for soft-biometrics
estimation. A comprehensive study of the effects of different pre-training over
the employed architectures is carried out, showing that, in most cases, a
better accuracy is obtained after the networks have been fine-tuned for face
recognition.
