Open Access

ARTICLE

IDSSCNN-XgBoost: Improved Dual-Stream Shallow Convolutional Neural Network Based on Extreme Gradient Boosting Algorithm for Micro Expression Recognition

by Adnan Ahmad, Zhao Li^*, Irfan Tariq, Zhengran He

School of Information Science and Engineering, Southeast University, Nanjing, 210016, China

* Corresponding Author: Zhao Li. Email:

Computers, Materials & Continua 2025, 82(1), 729-749. https://doi.org/10.32604/cmc.2024.055768

Received 06 July 2024; Accepted 18 October 2024; Issue published 03 January 2025

Abstract

Micro-expressions (ME) recognition is a complex task that requires advanced techniques to extract informative features from facial expressions. Numerous deep neural networks (DNNs) with convolutional structures have been proposed. However, unlike DNNs, shallow convolutional neural networks often outperform deeper models in mitigating overfitting, particularly with small datasets. Still, many of these methods rely on a single feature for recognition, resulting in an insufficient ability to extract highly effective features. To address this limitation, in this paper, an Improved Dual-stream Shallow Convolutional Neural Network based on an Extreme Gradient Boosting Algorithm (IDSSCNN-XgBoost) is introduced for ME Recognition. The proposed method utilizes a dual-stream architecture where motion vectors (temporal features) are extracted using Optical Flow TV-L1 and amplify subtle changes (spatial features) via Eulerian Video Magnification (EVM). These features are processed by IDSSCNN, with an attention mechanism applied to refine the extracted effective features. The outputs are then fused, concatenated, and classified using the XgBoost algorithm. This comprehensive approach significantly improves recognition accuracy by leveraging the strengths of both temporal and spatial information, supported by the robust classification power of XgBoost. The proposed method is evaluated on three publicly available ME databases named Chinese Academy of Sciences Micro-expression Database (CASMEII), Spontaneous Micro-Expression Database (SMIC-HS), and Spontaneous Actions and Micro-Movements (SAMM). Experimental results indicate that the proposed model can achieve outstanding results compared to recent models. The accuracy results are 79.01%, 69.22%, and 68.99% on CASMEII, SMIC-HS, and SAMM, and the F1-score are 75.47%, 68.91%, and 63.84%, respectively. The proposed method has the advantage of operational efficiency and less computational time.

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