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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. [ABSTRACT FROM AUTHOR]

Abstract: This paper provides a new age estimation approach, which distinguishes itself with the following three contributions. First, we combine distance metric learning and dimensionality reduction to better explore the connections between facial features and age labels. Second, to exploit the intrinsic ordinal relationship among human ages and overcome the potential data imbalance problem, a label-sensitive concept and several imbalance treatments are introduced in the system training phase. Finally, an age-oriented local regression is presented to capture the complicated facial aging process for age determination. The simulation results show that our approach achieves the lowest estimation error against existing methods. [Copyright &y& Elsevier]