Abstract: Human child trafficking has become a global epidemic with over 10 million children forced into labor or prostitution. In this paper, we propose the ChildrEN SafEty and Rescue (CENSER) system used by the Guria non-profit organization to retrieve trafficked children from brothels in India. The CENSER system is formed of the proposed Memory Augmented ScatterNet ResNet Hybrid (MSRHN) network trained on three databases containing images of trafficked children at different ages, their kins, and their sketches. The CENSER system encodes the input image of a child using the proposed Memory Augmented ScatterNet ResNet Hybrid (MSRHN) network and queries the encoding with the (i) Age, (ii) Kinship, and (iii) Sketch databases to establish the child's identity. The CENSER system can also predict if a child is a minor, which is used along with their identity to convince law enforcement to initiate the rescue operation. The MSRHN network is pre-trained on the KinFace database and then fine-tuned on the three databases. The performance of the proposed model is compared with several state-of-the-art methods.