Facial expression recognition based on improved local binary pattern and class-regularized locality preserving projection

This paper provides a novel method for facial expression recognition, which distinguishes itself with the following two main contributions. First, an improved facial feature, called the expression-specific local binary pattern (es-LBP), is presented by emphasizing the partial information of human faces on particular fiducial points. Second, to enhance the connection between facial features and expression classes, class-regularized locality preserving projection (cr-LPP) is proposed, which aims at maximizing the class independence and simultaneously preserving the local feature similarity via dimensionality reduction. Simulation results show that the proposed approach is very effective for facial expression recognition. Display Omitted The es-LBP, the es-LBP-s, and cr-LPP are proposed for facial expression recognition.The es-LBP computes the local LBP histograms around some particular fiducial points.To further include the spatial information, symmetric extension is also applied.Cr-LPP can enhance the connection between facial features and expressions.Simulations show that the proposed algorithm achieves the highest recognition rate.