Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …

Metric learning aims to measure the similarity among samples while using an optimal
distance metric for learning tasks. Metric learning methods, which generally use a linear …

Learning discriminative features for Facial Expression Recognition (FER) in the wild using
Convolutional Neural Networks (CNNs) is a non-trivial task due to the significant intra-class …

Facial expression recognition (FER) in the wild received broad concerns in which occlusion
and pose variation are two key issues. This paper proposed a global multi-scale and local …

With the transition of facial expression recognition (FER) from laboratory-controlled to
challenging in-the-wild conditions and the recent success of deep learning techniques in …

In this work, we present a framework to measure and mitigate intrinsic biases with respect to
protected variables-such as gender-in visual recognition tasks. We show that trained models …

Automated Facial Expression Recognition (FER) in the wild using deep neural networks is
still challenging due to intra-class variations and inter-class similarities in facial images …

We present an approach that combines automatic features learned by convolutional neural
networks (CNN) and handcrafted features computed by the bag-of-visual-words (BOVW) …

An image is worth a thousand words; hence, a face image illustrates extensive details about
the specification, gender, age, and emotional states of mind. Facial expressions play an …

Speech Emotion Recognition (SER) refers to the use of machines to recognize the emotions
of a speaker from his (or her) speech. SER benefits Human-Computer Interaction (HCI). But …