Expression Recognition Method Based on a Lightweight Convolutional Neural Network
Open accessAbstract
Effective emotion recognition algorithms can help machines better understand people and promote the development of human-computer interaction applications. In recent years, many research efforts have used benchmark expression data to train deep neural network models to achieve state-of-art results. These high-accuracy models usually contain hundreds of layers, so they require complex calculations and may not be suitable for real-world scenarios. This paper proposes a lightweight emotion recognition (LER) model to handle the latency problem under natural conditions. The three main contributions of this paper are as follows. 1) The LER model incorporates a densely connected convolution layer and model compression techniques into a framework that eliminates redundancy parameters. 2) Multichannel input is introduced in our work to preprocess the image data, which improves the learning ability of the model. 3) Experiments show that the proposed LER model has better performance on the FER2013 and FERPLUS datasets compared with other lightweight models. Compared with the VGG13 used in previous work, the LER model achieves higher accuracy and reduces the number of parameters by 97 times. Finally, the FERFIN dataset is created, which had fewer noise data and more accurate labels than the FERPLUS dataset.
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Compared with the methods in [43] and [44], the proposed method uses fewer resources.
“Compared with the methods in [43] and [44], the proposed method uses fewer resources.”
From A Resource Efficient Integer-Arithmetic-Only FPGA-Based CNN Accelerator for Real-Time Facial Emotion Recognition (2021) · cites this paper as [43]
In D. Comparison with State-of-the-Art (page unavailable), the paper reports that its DenseNet models have fewer parameters than VGG13 and says they achieve the best tradeoff between accuracy and latency among the models tested. Our copy does not establish that the proposed method uses fewer resources than both methods labeled [43] and [44] in the citing document; those references cannot be identified from the sentence alone. (LLM, conf=0.76)
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- Authors
- Zhao, G., Yang, H., & Yu, M.
- Affiliations
- Beijing University of Civil Engineering and Architecture (CN)
- Guangdong Academy of Medical Sciences (CN)
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 38528-38537
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2998896153
- Cited by
- 53
- References
- 44
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 12 · 2023: 13 · 2022: 7 · 2021: 9 · 2020: 1
- FWCI
- 7.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (61871021, 61531006)
- 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, emotion recognition, lightweight convolutional neural network, model compression, parameter reduction, inference latency, FER2013, human-computer interaction
About these checks
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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