Cognitive facial expression recognition with constrained dimensionality reduction

Facial expression recognition (FER) is an important research area in human-computer interaction. In this paper, a new dimensionality reduction method together with a new classifier are proposed for FER. The goals of most dimensionality reduction contains minimizing the within-class distances. However, the within-class distances for some expressions could be very large, so that to minimize these distances could largely influence the optimization function. To overcome this defect, a new dimensionality reduction method is proposed by adding a penalty item, which is the sum of within distances that are far from each other. Through maximizing this item, the distances among faces with the same expression that are far from each other cannot be minimized to too small. Besides, this method can partly characterize the density information from training samples. To make full use of density information, a new classification method is developed that is based on the enhanced cognitive gravity model. The conducted experiments validate the proposed approach in term of the performance of facial expression recognition. The approach presents the excellent performance over previously available techniques.