The POSTER has achieved SOTA performance in facial expression recognition (FER) by
effectively combining facial landmarks and image features through its two-stream pyramid …

Automatic machine-based Facial Expression Analysis (FEA) has made substantial progress
in the past few decades driven by its importance for applications in psychology, security …

Facial Expression Recognition (FER) in the wild is extremely challenging due to occlusions,
variant head poses, face deformation and motion blur under unconstrained conditions …

Over the past few years, Convolutional Neural Networks (CNNs) have shown promise on
facial expression recognition. However, the performance degrades dramatically under real …

Automatic affect analysis has attracted great interest in various contexts including the
recognition of action units and basic or non-basic emotions. In spite of major efforts, there …

Extraction of discriminative features from salient facial patches plays a vital role in effective
facial expression recognition. The accurate detection of facial landmarks improves the …

Facial expression recognition (FER) is a challenging task due to different expressions under
arbitrary poses. Most conventional approaches either perform face frontalization on a non …

In this paper, a novel deep neural network (DNN)-driven feature learning method is
proposed and applied to multi-view facial expression recognition (FER). In this method …

Facial expression recognition (FER) in the wild is a novel and challenging topic in the field of
human emotion perception. Different kinds of convolutional neural network (CNN) …

In this paper, an effective approach for the feature extraction of raw Electroencephalogram
(EEG) signals by means of one-dimensional local binary pattern (1D-LBP) was presented …