  Child trafficking in a serious problem around the world. Every year there are
more than 4 million victims of child trafficking around the world, many of them
for the purposes of child sexual exploitation. In collaboration with UK Police
and a non-profit focused on child abuse prevention, Global Emancipation
Network, we developed a proof-of-concept machine learning pipeline to aid the
identification of children from intercepted images. In this work, we focus on
images that contain children wearing school uniforms to identify the school of
origin. In the absence of a machine learning pipeline, this hugely time
consuming and labor intensive task is manually conducted by law enforcement
personnel. Thus, by automating aspects of the school identification process, we
hope to significantly impact the speed of this portion of child identification.
Our proposed pipeline consists of two machine learning models: i) to identify
whether an image of a child contains a school uniform in it, and ii)
identification of attributes of different school uniform items (such as
color/texture of shirts, sweaters, blazers etc.). We describe the data
collection, labeling, model development and validation process, along with
strategies for efficient searching of schools using the model predictions.
