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- CASME II: An Improved Spontaneous Micro-Expression Database and the Baseline Evaluation990
An improved micro-expression recognition system, CASME II, is built, with higher temporal resolution (200 fps) and spatial resolution (about 280×340 pixels on facial area) and elicited participants' facial expressions in a well-controlled laboratory environment and proper illumination.
- Facial expression recognition from near-infrared videos778
A novel research on a dynamic facial expression recognition, using near-infrared (NIR) video sequences and LBP-TOP feature descriptors and component-based facial features are presented to combine geometric and appearance information, providing an effective way for representing the facial expressions.
- Computer Vision Using Local Binary Patterns772
Computer Vision Using Local Binary Patterns provides a detailed description of the LBP methods and their variants both in spatial and spatiotemporal domains and provides an excellent overview as to how texture methods can be utilized for solving different kinds of computer vision and image analysis problems.
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- Searching Central Difference Convolutional Networks for Face Anti-Spoofing637
A novel frame level FAS method based on Central Difference Convolution (CDC), which is able to capture intrinsic detailed patterns via aggregating both intensity and gradient information is proposed.
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- Deep Affect Prediction in-the-Wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond527
The Aff-Wild benchmark for training and evaluating affect recognition algorithms is introduced and an end-to-end deep neural architecture which performs prediction of continuous emotion dimensions based on visual cues is designed and extensively train.
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- From BoW to CNN: Two Decades of Texture Representation for Texture Classification375
A comprehensive survey of advances in texture representation over the last two decades is presented covering different aspects of the research, including benchmark datasets and state of the art results.
- Learning Graph Convolutional Network for Skeleton-Based Human Action Recognition by Neural Searching371
The first automatically designed GCN for skeleton-based action recognition is proposed and enriched by providing multiple dynamic graph modules after fully exploring the spatial-temporal correlations between nodes, and a sampling- and memory-efficient evolution strategy is proposed to search an optimal architecture for this task.
- PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer343
This paper proposes the PhysFormer, an end-to-end video transformer based architecture, to adaptively aggregate both local and global spatio-temporal features for rPPG representation enhancement, and proposes the label distribution learning and a curriculum learning inspired dynamic constraint in frequency domain.
- Remote Heart Rate Measurement From Highly Compressed Facial Videos: An End-to-End Deep Learning Solution With Video Enhancement341
Comprehensive experiments are performed to show that the proposed method not only achieves superior performance on compressed videos with high-quality videos pair, but also generalizes well on novel data with only compressed videos available, which implies the promising potential for real-world applications.
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- Spontaneous facial micro-expression analysis using Spatiotemporal Completed Local Quantized Patterns303
Experimental results demonstrate that STCLQP achieves a substantial improvement for analyzing facial micro-expressions, and an efficient vector quantization and discriminative codebook selection is proposed to make LBP-TOP more discrim inative and compact.
- Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-Expressions290
A novel deep recurrent convolutional networks based micro-expression recognition approach, capturing the spatiotemporal deformations of micro- expression sequence and optimized by an end-to-end manner and obviates manual feature design is proposed.
- Deep Learning for Face Anti-Spoofing: A Survey275
This paper presents the first comprehensive review of recent advances in deep learning based FAS and collects and analyze the latest methods specially designed for domain generalization and open-set FAS.
- NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing275
This work proposes the first FAS method based on neural architecture search (NAS), called NAS-FAS, to discover the well-suited task-aware networks, and develops a novel search space consisting of central difference convolution and pooling operators.
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- Video-Based Remote Physiological Measurement via Cross-Verified Feature Disentangling244
A cross-verified feature disentangling strategy to disentangle the physiological features with non-physiological representations, and then use the distilled physiological features for robust multi-task physiological measurements.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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