Region Based Robust Facial Expression Analysis
Abstract
Facial emotion recognition is an essential aspect in human-machine interaction. In the real-world conditions, it faces many challenges, i.e., illumination changes, large pose variations and partial or full occlusions, which cause different facial areas with different sharpness and completeness. Inspired by this fact, we focus on facial expression recognition based on partial faces in this paper. We compare contribution of seven facial areas of low-resolution images, including nose areas, mouse areas, eyes areas, nose to mouse areas, nose to eyes areas, mouth to eyes areas and the whole face areas. Through analysis on the confusion matrix and the class activation map, we find that mouth regions contain much emotional information compared with nose areas and eyes areas. In the meantime, considering larger facial areas is helpful to judge the expression more precisely. To sum up, contributions of this paper are two-fold: (1) We reveal concerned areas of human in emotion recognition. (2) We quantify the contribution of different facial parts.
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Compared with the method of [22], the proposed method exhibits 5.07% higher accuracy.
“In a comparison with the method of [22], it was not possible to measure the number of operations and parameters, but the proposed method exhibits 5.07% higher accuracy.”
From A Resource Efficient Integer-Arithmetic-Only FPGA-Based CNN Accelerator for Real-Time Facial Emotion Recognition (2021) · cites this paper as [22]
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Recognizing Facial Expressions Using a Shallow Convolutional Neural Network
Miao, S., Xu, H., Han, Z., & Zhu, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2921220 · Cited by 110 · see citing works
Kim, J., Kang, J.-K., & Kim, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3099075 · Cited by 50 · see citing works
- Authors
- Lian, Z., Li, Y., Tao, J., Huang, J., & Niu, M.
- Affiliations
- Chinese Academy of Sciences (CN)
- Venue
- 2018 First Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia)
- Publisher
- IEEE
- Published
- 2018-05
- Pages
- 1-5
- OpenAlex
- W2894268739
- Cited by
- 14
- References
- 4
- Citations by year
- 2024: 3 · 2023: 1 · 2022: 4 · 2021: 4 · 2019: 2
- FWCI
- 1.83
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia) (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- facial expression recognition, facial emotion recognition, region-based analysis, partial face recognition, oral region, occlusion, low-resolution images, class activation maps, confusion matrix analysis
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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