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