Efficient Dynamic Emotion Recognition from Facial Expressions Using Statistical Spatio-Temporal Geometric Features
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| Izdano u: | Big Data and Cognitive Computing vol. 9, no. 8 (2025), p. 213-236 |
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| Glavni autor: | |
| Izdano: |
MDPI AG
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| Teme: | |
| Online pristup: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2504-2289 | ||
| 024 | 7 | |a 10.3390/bdcc9080213 |2 doi | |
| 035 | |a 3243980484 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Yaddaden Yacine | |
| 245 | 1 | |a Efficient Dynamic Emotion Recognition from Facial Expressions Using Statistical Spatio-Temporal Geometric Features | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Automatic Facial Expression Recognition (AFER) is a key component of affective computing, enabling machines to recognize and interpret human emotions across various applications such as human–computer interaction, healthcare, entertainment, and social robotics. Dynamic AFER systems, which exploit image sequences, can capture the temporal evolution of facial expressions but often suffer from high computational costs, limiting their suitability for real-time use. In this paper, we propose an efficient dynamic AFER approach based on a novel spatio-temporal representation. Facial landmarks are extracted, and all possible Euclidean distances are computed to model the spatial structure. To capture temporal variations, three statistical metrics are applied to each distance sequence. A feature selection stage based on the Extremely Randomized Trees (ExtRa-Trees) algorithm is then performed to reduce dimensionality and enhance classification performance. Finally, the emotions are classified using a linear multi-class Support Vector Machine (SVM) and compared against the k-Nearest Neighbors (k-NN) method. The proposed approach is evaluated on three benchmark datasets: CK+, MUG, and MMI, achieving recognition rates of 94.65%, 93.98%, and 75.59%, respectively. Our results demonstrate that the proposed method achieves a strong balance between accuracy and computational efficiency, making it well-suited for real-time facial expression recognition applications. | |
| 610 | 4 | |a CNN | |
| 653 | |a Robotics | ||
| 653 | |a Face recognition | ||
| 653 | |a Accuracy | ||
| 653 | |a Datasets | ||
| 653 | |a Deep learning | ||
| 653 | |a Wavelet transforms | ||
| 653 | |a Affective computing | ||
| 653 | |a Support vector machines | ||
| 653 | |a Emotion recognition | ||
| 653 | |a Communication | ||
| 653 | |a Pattern recognition systems | ||
| 653 | |a Neural networks | ||
| 653 | |a Computational efficiency | ||
| 653 | |a Computing costs | ||
| 653 | |a Feature selection | ||
| 653 | |a Emotions | ||
| 653 | |a Real time | ||
| 653 | |a Efficiency | ||
| 773 | 0 | |t Big Data and Cognitive Computing |g vol. 9, no. 8 (2025), p. 213-236 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3243980484/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3243980484/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3243980484/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |