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Expression neutralization is the process of synthetically altering an image of a face so as to remove any facial expression from it without changing the face's identity. Facial expression neutralization could have a variety of applications, particularly in the realms of facial recognition, in action unit analysis, or even improving the quality of identification pictures for various types of documents. Our proposed model, StoicNet, combines the robust encoding capacity of variational autoencoders, the generative power of generative adversarial networks, and the enhancing capabilities of super resolution networks with a learned encoding transformation to achieve compelling expression neutralization, while preserving the identity of the input face. Objective experiments demonstrate that StoicNet successfully generates realistic, identity-preserved faces with neutral expressions, regardless of the emotion or expression intensity of the input face.