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853 results on '"Cheng,Wen"'

5. CA-FER: Mitigating Spurious Correlation With Counterfactual Attention in Facial Expression Recognition.

Author

Huang, Pin-Jui, Xie, Hongxia, Huang, Hung-Cheng, Shuai, Hong-Han, and Cheng, Wen-Huang

Abstract

Although facial expression recognition based on deep learning has become a major trend, existing methods have been found to prefer learning spurious statistical correlations and non-robust features during training. This degenerates the model's generalizability in practical situations. One of the research fields mitigating such misperception of correlations as causality is causal reasoning. In this article, we propose a learnable counterfactual attention mechanism, CA-FER, that uses causal reasoning to simultaneously optimize feature discrimination and diversity to mitigate spurious correlations in expression datasets. To the best of our knowledge, this is the first work to study the spurious correlations in facial expression recognition from a counterfactual attention perspective. Extensive experiments on a synthetic dataset and four public datasets demonstrate that our method outperforms previous methods, which shows the effectiveness and generalizability of our learnable counterfactual attention mechanism for the expression recognition task. [ABSTRACT FROM AUTHOR]

Published
2024
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14. Modeling Uncertainty for Low-Resolution Facial Expression Recognition.

Author

Lo, Ling, Ruan, Bo-Kai, Shuai, Hong-Han, and Cheng, Wen-Huang

Abstract

Recently, facial expression recognition techniques have made significant progress on high-resolution web images. However, in real-world applications, the obtained images are often with low resolution since they are mostly captured in a wide range of public spaces. As a result, the ambiguity of the expression labels hinders recognition performance due to not only subjective emotion annotations but also ambiguous images. Existing approaches tend to perform poorly when the resolution of face images decreases. In this work, we aim to model the aleatoric uncertainty induced by low-image-resolution and label ambiguity for robust facial expression recognition. We propose probabilistic data uncertainty learning to capture the ambiguity induced by poor image resolution. Additionally, we introduce the emotion wheel to learn the label-uncertainty-aware embedding. Moreover, we exploit the ambiguous nature of neutrality and propose a neutral expression constraint to learn more robust features for facial expression recognition. To the best of our knowledge, this is the first work utilizing the intrinsic nature of neutrality as a regularization to benefit model training. Extensive experimental results show the effectiveness and robustness of our approach. Under low-resolution conditions, our proposed method outperforms the state-of-the-art approaches by 3.02% and 3.16% in terms of accuracy on RAF-DB and FERPlus, respectively. [ABSTRACT FROM AUTHOR]

Published
2024
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26. Fast Vehicle Detection and Tracking on Fisheye Traffic Monitoring Video Using CNN and Bounding Box Propagation

Author

Ardianto, Sandy, Hang, Hsueh-Ming, and Cheng, Wen-Huang

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Computer Science - Computer Vision and Pattern Recognition
Abstract

We design a fast car detection and tracking algorithm for traffic monitoring fisheye video mounted on crossroads. We use ICIP 2020 VIP Cup dataset and adopt YOLOv5 as the object detection base model. The nighttime video of this dataset is very challenging, and the detection accuracy (AP50) of the base model is about 54%. We design a reliable car detection and tracking algorithm based on the concept of bounding box propagation among frames, which provides 17.9 percentage points (pp) and 6.2 pp. accuracy improvement over the base model for the nighttime and daytime videos, respectively. To speed up, the grayscale frame difference is used for the intermediate frames in a segment, which can double the processing speed., to be published in International Conference on Image Processing (ICIP) 2022, Bordeaux, France

Published
2022
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43. An Overview of Facial Micro-Expression Analysis: Data, Methodology and Challenge.

Author

Xie, Hong-Xia, Lo, Ling, Shuai, Hong-Han, and Cheng, Wen-Huang

Abstract

Facial micro-expressions indicate brief and subtle facial movements that appear during emotional communication. In comparison to macro-expressions, micro-expressions are more challenging to be analyzed due to the short span of time and the fine-grained changes. In recent years, micro-expression recognition (MER) has drawn much attention because it can benefit a wide range of applications, e.g., police interrogation, clinical diagnosis, depression analysis, and business negotiation. In this survey, we offer a fresh overview to discuss new research directions and challenges these days for MER tasks. For example, we review MER approaches from three novel aspects: macro-to-micro adaptation, recognition based on key apex frames, and recognition based on facial action units. Moreover, to mitigate the problem of limited and biased ME data, synthetic data generation is surveyed for the diversity enrichment of micro-expression data. Since micro-expression spotting can boost micro-expression analysis, the state-of-the-art spotting works are also introduced in this paper. At last, we discuss the challenges in MER research and provide potential solutions as well as possible directions for further investigation. [ABSTRACT FROM AUTHOR]

Published
2023
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