  By utilizing label distribution learning, a probability distribution is
assigned for a facial image to express a compound emotion, which effectively
improves the problem of label uncertainties and noises occurred in one-hot
labels. In practice, it is observed that correlations among emotions are
inherently different, such as surprised and happy emotions are more possibly
synchronized than surprised and neutral. It indicates the correlation may be
crucial for obtaining a reliable label distribution. Based on this, we propose
a new method that amends the label distribution of each facial image by
leveraging correlations among expressions in the semantic space. Inspired by
inherently diverse correlations among word2vecs, the topological information
among facial expressions is firstly explored in the semantic space, and each
image is embedded into the semantic space. Specially, a class-relation graph is
constructed to transfer the semantic correlation among expressions into the
task space. By comparing semantic and task class-relation graphs of each image,
the confidence of its label distribution is evaluated. Based on the confidence,
the label distribution is amended by enhancing samples with higher confidence
and weakening samples with lower confidence. Experimental results demonstrate
the proposed method is more effective than compared state-of-the-art methods.
