A review of affective computing: From unimodal analysis to multimodal fusion
Affective computing is an emerging interdisciplinary research field bringing together
researchers and practitioners from various fields, ranging from artificial intelligence, natural …
researchers and practitioners from various fields, ranging from artificial intelligence, natural …
Survey on rgb, 3d, thermal, and multimodal approaches for facial expression recognition: History, trends, and affect-related applications
CA Corneanu, MO Simón, JF Cohn… - IEEE transactions on …, 2016 - ieeexplore.ieee.org
Facial expressions are an important way through which humans interact socially. Building a
system capable of automatically recognizing facial expressions from images and video has …
system capable of automatically recognizing facial expressions from images and video has …
Facial expression recognition via learning deep sparse autoencoders
Facial expression recognition is an important research issue in the pattern recognition field.
In this paper, we intend to present a novel framework for facial expression recognition to …
In this paper, we intend to present a novel framework for facial expression recognition to …
American sign language recognition using deep learning and computer vision
K Bantupalli, Y Xie - 2018 IEEE international conference on big …, 2018 - ieeexplore.ieee.org
Speech impairment is a disability which affects an individuals ability to communicate using
speech and hearing. People who are affected by this use other media of communication …
speech and hearing. People who are affected by this use other media of communication …
Stretchable, transparent, ultrasensitive, and patchable strain sensor for human–machine interfaces comprising a nanohybrid of carbon nanotubes and conductive …
Interactivity between humans and smart systems, including wearable, body-attachable, or
implantable platforms, can be enhanced by realization of multifunctional human–machine …
implantable platforms, can be enhanced by realization of multifunctional human–machine …
Deep feature consistent variational autoencoder
We present a novel method for constructing Variational Autoencoder (VAE). Instead of using
pixel-by-pixel loss, we enforce deep feature consistency between the input and the output of …
pixel-by-pixel loss, we enforce deep feature consistency between the input and the output of …
Object detection from video tubelets with convolutional neural networks
Abstract Deep Convolution Neural Networks (CNNs) have shown impressive performance in
various vision tasks such as image classification, object detection and semantic …
various vision tasks such as image classification, object detection and semantic …
Automatic facial expression recognition using features of salient facial patches
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. The accurate detection of facial landmarks improves the …
Facial expression recognition using enhanced deep 3D convolutional neural networks
Abstract Deep Neural Networks (DNNs) have shown to outperform traditional methods in
various visual recognition tasks including Facial Expression Recognition (FER). In spite of …
various visual recognition tasks including Facial Expression Recognition (FER). In spite of …
A body sensor data fusion and deep recurrent neural network-based behavior recognition approach for robust healthcare
Recently, human healthcare from body sensor data has been getting remarkable research
attentions by a huge range of human-computer interaction and pattern analysis researchers …
attentions by a huge range of human-computer interaction and pattern analysis researchers …