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The real-world facial expression recognition (FER) datasets suffer from noisyannotations due to crowd-sourcing, ambiguity in expressions, the subjectivityof annotators and inter-class similarity. However, the recent deep networkshave strong capacity to memorize the noisy annotations leading to corruptedfeature embedding and poor generalization. To handle noisy annotations, wepropose a dynamic FER learning framework (DNFER) in which clean samples areselected based on dynamic class specific threshold during training.Specifically, DNFER is based on supervised training using selected cleansamples and unsupervised consistent training using all the samples. Duringtraining, the mean posterior class probabilities of each mini-batch is used asdynamic class-specific threshold to select the clean samples for supervisedtraining. This threshold is independent of noise rate and does not need anyclean data unlike other methods. In addition, to learn from all samples, theposterior distributions between weakly-augmented image and strongly-augmentedimage are aligned using an unsupervised consistency loss. We demonstrate therobustness of DNFER on both synthetic as well as on real noisy annotated FERdatasets like RAFDB, FERPlus, SFEW and AffectNet.

Dynamic Adaptive Threshold based Learning for Noisy Annotations Robust Facial Expression Recognition