As a sub-challenge of EmotiW (the Emotion Recognition in the Wild challenge), how to
improve performance on the AFEW (Acted Facial Expressions in the wild) dataset is a …

This paper presents a novel facial expression recognition network, called Distract your
Attention Network (DAN). Our method is based on two key observations in biological visual …

This paper proposes a new method based on a multiple branch cross-connected
convolutional neural network (MBCC-CNN) for facial expression recognition. Compared with …

Recognizing human expression in videos is a challenging task due to dynamic changes in
facial actions and diverse visual appearances. The key to design a reliable video-based …

In this letter, we propose a novel lightweight network HiNet: hybrid inherited feature learning
network and its variants: HiNet-ReLU, HiNet-Concat, Large Scale HiNet, 2 stack HiNet and 4 …

Abstract Facial Expression Recognition (FER) is a challenging task due to the complex
properties of human facial expression. Recently, convolutional neural networks (CNNs) …

In complex real-world situations, problems such as illumination changes, facial occlusion,
and variant poses make facial expression recognition (FER) a challenging task. To solve the …

One key challenge in facial expression recognition (FER) is the extraction of discriminative
features from critical facial regions. Because of their promising ability to learn discriminative …

In spite of the recent advancements in the field of deep learning based techniques for facial
expression recognition, the efficiency of the state-of-the-art recognition methods in the wild …

Facial expression recognition (FER) in the wild is a challenging task for affective computing
in human–machine interaction fields. However, most of the existing methods fail to learn the …