Measuring and comparing facial expression have several practical applications. One such application is to measure the facial expression embedding, and to compare distances between those expressions embeddings in order to determine the identity- and face swapping algorithms' capabilities in preserving the facial expression information. One useful aspect is to present how well the expressions are preserved while anonymizing facial data during privacy aware data collection. We show that a weighted supervised contrastive learning is a strong approach for learning facial expression representation embeddings and dealing with the class imbalance bias. By feeding a classifier-head with the learned embeddings we reach competitive state-of-the-art results. Furthermore, we demonstrate the use case of measuring the distance between the expressions of a target face, a source face and the anonymized target face in the facial anonymization context. © 2021 IEEE.