  The collection of a lot of personal information about individuals, including
the minor members of a family, by closed-circuit television (CCTV) cameras
creates a lot of privacy concerns. Particularly, revealing children's
identifications or activities may compromise their well-being. In this paper,
we investigate lightweight solutions that are affordable to edge surveillance
systems, which is made feasible and accurate to identify minors such that
appropriate privacy-preserving measures can be applied accordingly. State of
the art deep learning architectures are modified and re-purposed in a cascaded
fashion to maximize the accuracy of our model. A pipeline extracts faces from
the input frames and classifies each one to be of an adult or a child. Over
20,000 labeled sample points are used for classification. We explore the timing
and resources needed for such a model to be used in the Edge-Fog architecture
at the edge of the network, where we can achieve near real-time performance on
the CPU. Quantitative experimental results show the superiority of our proposed
model with an accuracy of 92.1% in classification compared to some other face
recognition based child detection approaches.
