Face and Expression Recognition
251 papers on this topic
Facial Expression Analysis and Its Potentials in IoT Systems: A Contemporary Survey
Facial expressions convey human emotions and can be categorized into macro-expressions (MaEs) and micro-expressions (MiEs) based on duration and intensity. While MaEs are voluntary and easily...
One-step Multi-view Clustering with Diverse Representation
Multi-view clustering has attracted broad attention due to its capacity to utilize consistent and complementary information among views. Although tremendous progress has been made recently, most...
One Swallow Does Not Make a Summer: Understanding Semantic Structures in Embedding Spaces
Embedding spaces are fundamental to modern AI, translating raw data into high-dimensional vectors that encode rich semantic relationships. Yet, their internal structures remain opaque, with...
Suppress and Rebalance: Towards Generalized Multi-Modal Face Anti-Spoofing
Face Anti-Spoofing (FAS) is crucial for securing face recognition systems against presentation attacks. With advancements in sensor manufacture and multi-modal learning techniques, many...
MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wild
Dynamic Facial Expression Recognition (DFER) has received significant interest in the recent years dictated by its pivotal role in enabling empathic and human-compatible technologies. Achieving...
ExpLLM: Towards Chain of Thought for Facial Expression Recognition
Facial expression recognition (FER) is a critical task in multimedia with significant implications across various domains. However, analyzing the causes of facial expressions is essential for...
QCS: Feature Refining from Quadruplet Cross Similarity for Facial Expression Recognition
Facial expression recognition faces challenges where labeled significant features in datasets are mixed with unlabeled redundant ones. In this paper, we introduce Cross Similarity Attention (CSA)...
GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition
Previous electroencephalogram (EEG) emotion recognition relies on single-task learning, which may lead to overfitting and learned emotion features lacking generalization. In this paper, a...
MARLIN: Masked Autoencoder for facial video Representation LearnINg
This paper proposes a self-supervised approach to learn universal facial representations from videos, that can transfer across a variety of facial analysis tasks such as Facial Attribute...
Exploring Facial Expression Recognition through Semi-Supervised Pretraining and Temporal Modeling
Facial Expression Recognition (FER) plays a crucial role in computer vision and finds extensive applications across various fields. This paper aims to present our approach for the upcoming 6th...
Low-Rank Tensor Function Representation for Multi-Dimensional Data Recovery
Since higher-order tensors are naturally suitable for representing multi-dimensional data in real-world, e.g., color images and videos, low-rank tensor representation has become one of the...
POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition
Facial expression recognition (FER) is an important task in computer vision, having practical applications in areas such as human-computer interaction, education, healthcare, and online...
A Comprehensive Survey on Spectral Clustering with Graph Structure Learning
Spectral clustering is a powerful technique for clustering high-dimensional data, utilizing graph-based representations to detect complex, non-linear structures and non-convex clusters. The...
Federated t-SNE and UMAP for Distributed Data Visualization
High-dimensional data visualization is crucial in the big data era and these techniques such as t-SNE and UMAP have been widely used in science and engineering. Big data, however, is often...
Sparse Autoencoders, Again?
Is there really much more to say about sparse autoencoders (SAEs)? Autoencoders in general, and SAEs in particular, represent deep architectures that are capable of modeling low-dimensional latent...
An Adaptive Framework for Multi-View Clustering Leveraging Conditional Entropy Optimization
Multi-view clustering (MVC) has emerged as a powerful technique for extracting valuable insights from data characterized by multiple perspectives or modalities. Despite significant advancements,...
Uncertain Facial Expression Recognition via Multi-task Assisted Correction
Deep models for facial expression recognition achieve high performance by training on large-scale labeled data. However, publicly available datasets contain uncertain facial expressions caused by...
The Impact of Balancing Real and Synthetic Data on Accuracy and Fairness in Face Recognition
Over the recent years, the advancements in deep face recognition have fueled an increasing demand for large and diverse datasets. Nevertheless, the authentic data acquired to create those datasets...