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Journals / International Journal of Informatics and Applied Mathematics / 2026 / Volume: 9 Issue: 1

Mixed texture descriptors for facial expression recognition

Chebah Ouafa(Badji Mokhtar annaba)
Chebah Anissa(University of Badji Mokhtar Annaba)
Pages
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Publication Date
2026-08-02
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Experimental results using CK+ and JAFFE database show that the proposed approach achieves superior recognition performance compared to the existing studies with classification accuracy of 97.62% and 93.84% respectively.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This article proposes a novel approach to recognize facial expression using a combination of three local texture descriptors such as Local Binary Pattern (LBP), Gradient Direction Pattern (GDP) and Local Directional Pattern (LDP).
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Key Findings & Data:Firstly, we need to perform the pre-process of face image, which includes face detection and face image cropping.
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Conclusion & Impact:Experimental results using CK+ and JAFFE database show that the proposed approach achieves superior recognition performance compared to the existing studies with classification accuracy of 97.62% and 93.84% respectively.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Facial expressions · Human Face · Emotional recognition

Abstract

This article proposes a novel approach to recognize facial expression using a combination of three local texture descriptors such as Local Binary Pattern (LBP), Gradient Direction Pattern (GDP) and Local Directional Pattern (LDP). Firstly, we need to perform the pre-process of face image, which includes face detection and face image cropping. Next, we calculate texture descriptors based on LBP, GDP and LDP operators. Histogram sequence concatenation is then applied to these descriptors after dividing the facial image into a number of non-overlapping blocks. Finally, the concatenated histograms represent an input to a Support Vector Machine (SVM), which will be used to classify facial images in one of the six universal expressions. Experimental results using CK+ and JAFFE database show that the proposed approach achieves superior recognition performance compared to the existing studies with classification accuracy of 97.62% and 93.84% respectively.

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Cite This Article

Ouafa, C., Anissa, C. (2026). Mixed texture descriptors for facial expression recognition. International Journal of Informatics and Applied Mathematics, Volume: 9 Issue: 1. https://doi.org/10.53508/ijiam.1602587

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