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Found 10 papers, 1 papers with code
A Unified Transformer-based Network for multimodal Emotion Recognition
We then present our UBVMT network which is trained to perform emotion recognition by combining the 2D image-based representation of the ECG/PPG signal and the facial expression features.
Fine-grained Vibration Based Sensing Using a Smartphone
VibroTag's accuracy is 37% higher than the average accuracy of 49. 25% achieved by one of the state-of-the-art IMUs based schemes, which we implemented for comparison with VibroTag.
Monitoring Browsing Behavior of Customers in Retail Stores via RFID Imaging
The key novelty of this paper is on achieving browsing behavior monitoring of multiple customers in front of display items by constructing coarse grained images via robust, analytical model-driven deep learning based, RFID imaging.
An Efficient Integration of Disentangled Attended Expression and Identity FeaturesFor Facial Expression Transfer andSynthesis
In this paper, we present an Attention-based Identity Preserving Generative Adversarial Network (AIP-GAN) to overcome the identity leakage problem from a source image to a generated face image, an issue that is encountered in a cross-subject facial expression transfer and synthesis process.
AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an App
Building on the prior work on cough-based diagnosis of respiratory diseases, we propose, develop and test an Artificial Intelligence (AI)-powered screening solution for COVID-19 infection that is deployable via a smartphone app.
Facial Expression Representation Learning by Synthesizing Expression Images
Representations used for Facial Expression Recognition (FER) usually contain expression information along with identity features.
Facial Expression Recognition
Facial Expression Recognition (FER)
+2
All-In-One: Facial Expression Transfer, Editing and Recognition Using A Single Network
In this paper, we present a unified architecture known as Transfer-Editing and Recognition Generative Adversarial Network (TER-GAN) which can be used: 1. to transfer facial expressions from one identity to another identity, known as Facial Expression Transfer (FET), 2. to transform the expression of a given image to a target expression, while preserving the identity of the image, known as Facial Expression Editing (FEE), and 3. to recognize the facial expression of a face image, known as Facial Expression Recognition (FER).
Facial Expression Recognition Using Human to Animated-Character Expression Translation
In this paper we present a novel Human-to-Animation conditional Generative Adversarial Network (HA-GAN) to overcome these two problems by using many (human faces) to one (animated face) mapping.
Facial Expression Recognition
Facial Expression Recognition (FER)
+2
Facial Expression Recognition Using Disentangled Adversarial Learning
This expression representation is disentangled from identity component by explicitly providing the identity code to the decoder part of DE-GAN.
Facial Expression Recognition
Facial Expression Recognition (FER)
+3
Brain MRI Segmentation using Rule-Based Hybrid Approach
Performance of these classifiers is investigated over different images of brain MRI and the variation in the performance of these classifiers is observed for different brain tissues.
