Micro-expressions (MEs) are involuntary facial expressions, short-lived (usually between 1/5 and 1/25 seconds), and important in the application of security, psychological tests, and forensics. The MEs are however difficult to identify because it occur quickly and also involve little movement of the muscles. The paper presents an Enhanced Micro-Expression Descriptor which incorporates Gabor Wavelet-based Local Binary Patterns (GW-LBP), Temporal Gradient Magnitude Histograms (TGMH), and Wavelet Transform (WT) to enhance ME recognition, by overcoming the weaknesses of traditional methods in illumination sensitivity and poor computing power. The algorithm involves the use of GW-LBP to extract spatial texture, TGMH to capture changes in temporal motion, and WT to analyze frequencies on a multiscale basis. This is achieved by classifying the fused feature set with an RBF kernel Support Vector Machine (SVM), which is optimized by down-sampling to a size manageable by resources (4096 dimensions) to provide a resource-efficient, real-time solution with application in edge computing. Benchmark dataset experimental results prove that the proposed method is better than the existing techniques with a recognition accuracy of 85.9%. This is a major boost compared to conventional procedures such as the LBP-TOP (67.5%) and CNN-based models (78.3%). Also, the Wavelet Transform option exploited the highest score in entropy (0.93), which implies that it can be highly used in real-time behavioral analysis, emotion detection, and security surveillance. The findings affirm that the hybrid approach, which incorporates spatial, temporal, and frequency characteristics, has a better performance than the existing ME recognition models.
A Fusion Framework for Micro-Expression Recognition Using Hierarchical Transformer Network with DEAC. International Journal of Intelligent Engineering and Systems. 2025;18(3):39–58.
2.
Li Y, Wei J, Liu Y, Kauttonen J, Zhao G. Deep Learning for Micro-Expression Recognition: A Survey. IEEE Transactions on Affective Computing. 2022;13(4):2028–46.
3.
Goh KM, Ng CH, Lim LL, Sheikh UU. Micro-expression recognition: an updated review of current trends, challenges and solutions. The Visual Computer. 2018;36(3):445–68.
4.
Vrij A, Fisher RP. Lie Detection and Nonverbal Behaviour: Present and Future. Body Language Communication. Springer Nature Switzerland; 2024. p. 377–98.
5.
Hao H, Wang S, Ben H, Hao Y, Wang Y, Wang. Hierarchical space-time attention for micro-expression recognition. 2024;
6.
Wang Z, Zhang K, Luo W, Sankaranarayana R. HTNet for micro-expression recognition. Neurocomputing. 2024;602:128196.
7.
Ben X, Ren Y, Zhang J, Wang SJ, Kpalma K, Meng W, et al. Video-based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2021;1–1.
8.
Tran TK, Vo QN, Hong X, Li X, Zhao G. Micro-expression spotting: A new benchmark. Neurocomputing. 2021;443:356–68.
9.
Ahmed S, Frikha M, Hussein TDH, Rahebi J. [Retracted] Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning. BioMed Research International. 2021;2021(1).
10.
Twum F, Missah YM, Oppong SO, Ussiph N. Textural Analysis for Medicinal Plants Identification Using Log Gabor Filters. IEEE Access. 2022;10:83204–20.
11.
Li HA, Fan J, Zhang J, Li Z, He D, Si M, et al. Facial Image Segmentation Based on Gabor Filter. Mathematical Problems in Engineering. 2021;2021:1–7.
12.
Niu B, Gao Z, Guo B. Facial Expression Recognition with LBP and ORB Features. Computational Intelligence and Neuroscience. 2021;2021(1).
13.
Xiong R, Huang X, Guo L, Zou X, Tian H. Seismic Attribute Extraction and Application Based on the Gabor Wavelet Transform. IEEE Access. 2024;12:17807–22.
14.
Kumar S, Sagar V, Punetha D. A comparative study on facial expression recognition using local binary patterns, convolutional neural network and frequency neural network. Multimedia Tools and Applications. 2023;82(16):24369–85.
15.
Zhao Y, Lu J, Zhang Y, Xie S. Birdsong Recognition Method Based on Gabor and LBP Feature Fusion. 2023 7th International Conference on Communication and Information Systems (ICCIS). IEEE; 2023. p. 79–85.
16.
Srivastava PK, Singh G, Kumar S, Jain NK, Bali V. Gabor Filter and Centre Symmetric-Local Binary Pattern based technique for forgery detection in images. Multimedia Tools and Applications. 2023;83(17):50157–95.
17.
Dileep D, Sreeni KG. Anomalous Event Detection in Crowd Scenes using Histogram of Optical Flow and Entropy. 2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS). IEEE; 2021. p. 1–6.
18.
Tian C, Zheng M, Zuo W, Zhang B, Zhang Y, Zhang D. Multi-stage image denoising with the wavelet transform. Pattern Recognition. 2023;134:109050.
19.
Multiscale Feature Extraction for Remote Sensing Image Analysis Using Discrete Wavelet Transform. Fusion: Practice and Applications. 2026;21(1).
20.
Kola DGR, Samayamantula SK. Facial expression recognition using singular values and wavelet‐based LGC‐HD operator. IET Biometrics. 2021;10(2):207–18.
21.
Perveen G, Ali SF, Ahmad J, Shahab S, Adnan M, Anjum M, et al. Multi-Stream Deep Convolution Neural Network With Ensemble Learning for Facial Micro-Expression Recognition. IEEE Access. 2023;11:118474–89.
22.
Li J, Dong Z, Lu S, Wang SJ, Yan WJ, Ma Y, et al. CAS(ME)<sup>3</sup>: A Third Generation Facial Spontaneous Micro-Expression Database with Depth Information and High Ecological Validity. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2022;1–1.
23.
Wang H, Zhou J, Liu X, Jia Y, Chen T. A cross-database micro-expression recognition framework based on meta-learning. Applied Intelligence. 2024;55(1).
The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.
,
