Recent deep learning based facial expression recognition (FER) methods are mostly driven
by the availability of large amount of training data. However, availability of such data is not …

Using lightweight networks for facial expression recognition (FER) is becoming an important
research topic in recent years. The key to the success of FER with lightweight networks is to …

The weight-adapted convolution neural network (WACNN) is proposed to extract
discriminative expression representations for recognizing facial expression. It aims to make …

In facial expression recognition applications, the classification accuracy decreases because
of the blur, illumination and localization problems in images. Therefore, a robust emotion …

Emotion recognition through facial expressions represents a relevant way to understand and
even predict the human behavior. Thus, it has been used in various fields such as human …

Facial expression recognition (FER) plays an important role in cognitive psychology
research. In FER studies, deep convolutional neural networks (CNNs) and attention …

Melanoma, a kind of skin cancer that is very risky, is distinguished by uncontrolled cell
multiplication. Melanoma detection is of the utmost significance in clinical practice because …

Facial expression analysis and recognition has gained popularity in the last few years for its
challenging nature and broad area of applications like HCI, pain detection, operator fatigue …

In recent years, the focus of facial expression recognition (FER) has gradually shifted from
laboratory settings to challenging natural scenes. This requires a great deal of real-world …

Emotions are the natural and easiest way of communication between human beings and
also plays a prominent role in day to day life. In current scenario, emotion recognition is the …