  Halal requirement in food is important for millions of Muslims worldwide
especially for meat and chicken products, insuring that slaughter houses adhere
to this requirement is a challenging task to do manually. In this paper a
method is proposed that uses a camera that takes images of slaughtered chicken
on the conveyor in a slaughter house, the images are then analyzed by a deep
neural network to classify if the image is of a halal slaughtered chicken or
not. However, traditional deep learning models require large amounts of data to
train on, which in this case these amounts of data were challenging to collect
especially the images of non-halal slaughtered chicken, hence this paper shows
how the use of one shot learning [1] and transfer learning [2] can reach high
accuracy on the few amounts of data that were available. The architecture used
is based on the Siamese neural networks architecture which ranks the similarity
between two inputs [3] while using the Xception network [4] as the twin
networks. We call it HalalNet. This work was done as part of SYCUT (syriah
compliant slaughtering system) which is a monitoring system that monitors the
halalness of the slaughtered chicken in a slaughter house. The data used to
train and validate HalalNet was collected from the Azain slaughtering site
(Semenyih, Selangor, Malaysia) containing images of both halal and non-halal
slaughtered chicken.
