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People can innately recognize human facial expressions in unnatural forms,such as when depicted on the unusual faces drawn in cartoons or when applied toan animal's features. However, current machine learning algorithms strugglewith out-of-domain transfer in facial expression recognition (FER). We proposea biologically-inspired mechanism for such transfer learning, which is based onnorm-referenced encoding, where patterns are encoded in terms of differencevectors relative to a domain-specific reference vector. By incorporatingdomain-specific reference frames, we demonstrate high data efficiency intransfer learning across multiple domains. Our proposed architecture providesan explanation for how the human brain might innately recognize facialexpressions on varying head shapes (humans, monkeys, and cartoon avatars)without extensive training. Norm-referenced encoding also allows the intensityof the expression to be read out directly from neural unit activity, similar toface-selective neurons in the brain. Our model achieves a classificationaccuracy of 92.15\% on the FERG dataset with extreme data efficiency. We trainour proposed mechanism with only 12 images, including a single image of eachclass (facial expression) and one image per domain (avatar). In comparison, theauthors of the FERG dataset achieved a classification accuracy of 89.02\% withtheir FaceExpr model, which was trained on 43,000 images.

Multi-Domain Norm-referenced Encoding Enables Data Efficient Transfer Learning of Facial Expression Recognition