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<p>Emotions are important because they facilitate social connections, enable wordless communication, and inform values-based decision-making. In the field of human-computer interaction, real-time facial emotion recognition is a popular area of study. The definition of emotion recognition is the ability to recognize human emotion. This study presents a comprehensive and modular pipeline for facial expression recognition using the CK+48 dataset. We evaluate and compare several approaches, including convolutional neural networks trained on raw images and on images processed with traditional feature extraction techniques: histogram of oriented gradients (HOG), local binary patterns (LBP), scale-invariant feature transform (SIFT), and Gabor filters achieved HOG (97.96%), LBP (95.43%), SIFT (96.95%), Gabor (98.47%) and without feature extraction (97.96%). Our results demonstrate the effectiveness of combining deep learning with robust feature engineering for facial expression recognition tasks.</p>

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Hybrid deep learning and feature-based approaches for facial expression recognition

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月5日

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  • Neha Chourasia,
  • Chhattar Singh Lamba,
  • Amit Kumar Gupta

摘要

Emotions are important because they facilitate social connections, enable wordless communication, and inform values-based decision-making. In the field of human-computer interaction, real-time facial emotion recognition is a popular area of study. The definition of emotion recognition is the ability to recognize human emotion. This study presents a comprehensive and modular pipeline for facial expression recognition using the CK+48 dataset. We evaluate and compare several approaches, including convolutional neural networks trained on raw images and on images processed with traditional feature extraction techniques: histogram of oriented gradients (HOG), local binary patterns (LBP), scale-invariant feature transform (SIFT), and Gabor filters achieved HOG (97.96%), LBP (95.43%), SIFT (96.95%), Gabor (98.47%) and without feature extraction (97.96%). Our results demonstrate the effectiveness of combining deep learning with robust feature engineering for facial expression recognition tasks.