This paper reviews our research on facial expression recognition using spatiotemporal descriptors, combining the appearance and motion together and describing the local transition features. Experiments show very
Facial Expression Recognition is one of the active research area in the field of Human Machine Interaction (HMI) because of its several applications such as human emotion analysis, stress level and lie detection. In this paper, an algorithm for facial expression recognition has b
We introduce a linear regression-based projection for multi-view facial expressions recognition (MFER) based on sparse features. While facial expression recognition (FER) approaches have become popular in frontal or near to frontal views, few papers demonstrate their results on a
Given the nonlinear manifold structure of facial images, a new kernel-based supervised manifold learning algorithm based on locally linear embedding (LLE), called discriminant kernel locally linear embedding (DKLLE), is proposed for facial expression recognition. The proposed DKL
Many methods have been developed based on extracting Local Binary Pattern features associating different classifying techniques in order to get more and more better effects of facial expression recognition. In this work, we propose a novel method for recognizing facial expression
In this work, we propose a novel approach to recognize facial expressions from static images. First, the local binary patterns (LBP) are used to efficiently represent the facial images and then the linear programming (LP) technique is adopted to classify seven facial expressions—
In this paper, we propose a hybrid statistical feature extractor, Local Haar Mean Binary Pattern (LHMBP). It extracts level-1 haar approximation coefficients and computes Local Mean Binary Pattern (LMBP) of it. LMBP code of pixel is obtained by weighting the thresholded neighbor