  This paper presents methods to identify the plastic waste based on its resin
identification code to provide an efficient recycling of post-consumer plastic
waste. We propose the design, training and testing of different machine
learning techniques to (i) identify a plastic waste that belongs to the known
categories of plastic waste when the system is trained and (ii) identify a new
plastic waste that do not belong the any known categories of plastic waste
while the system is trained. For the first case,we propose the use of one-shot
learning techniques using Siamese and Triplet loss networks. Our proposed
approach does not require any augmentation to increase the size of the database
and achieved a high accuracy of 99.74%. For the second case, we propose the use
of supervised and unsupervised dimensionality reduction techniques and achieved
an accuracy of 95% to correctly identify a new plastic waste.
