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 …

The ability to recognize facial expressions automatically enables novel applications in
human-computer interaction and other areas. Consequently, there has been active research …

Existing public face image datasets are strongly biased toward Caucasian faces, and other
races (eg, Latino) are significantly underrepresented. The models trained from such datasets …

The performance of a computer vision model depends on the size and quality of its training
data. Recent studies have unveiled previously-unknown composition biases in common …

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) …

The Multi-task Cascaded Convolutional Networks (MTCNN) has recently demonstrated
impressive results on jointly face detection and alignment. By using the hard sample ming …

Interpersonal relation defines the association, eg, warm, friendliness, and dominance,
between two or more people. We investigate if such fine-grained and high-level relation …

Facial Expression Recognition (FER) can be widely applied to various research areas, such
as mental diseases diagnosis and human social/physiological interaction detection. With the …

Large Vision-Language Models (LVLMs) show significant strides in general-purpose
multimodal applications such as visual dialogue and embodied navigation. However …

Facial expression recognition (FER) has received significant attention in the past decade
with witnessed progress, but data inconsistencies among different FER datasets greatly …