Multi-instance Feature Learning Based on Sparse Representation for Facial Expression Recognition

摘要

Usually, sparse representation is adopted to learn the intrinsic structure in label spaces to fulfil recognition tasks. In this paper, we propose a feature learning scheme based on sparse representation and validate its effectiveness taking facial expression recognition as a multi-instance learning problem. By introducing the sparse constraint with l_1 sparse reg-ularization, the proposed model learns the instance-specific feature based on label variance information. In this paper, we propose two schemes for denoting the label variance in multi-instance facial expression recognition. Experimental analysis shows that the sparse constraint is useful in feature learning when label variance is properly expressed and utilized. We successfully obtain the stable structure in the feature spaces with the sparse representation based on multi-instance feature learning.