对比传统特征与深度学习在表情识别中的表现，发现数据越复杂越需深度模型。
An Empirical Study of Handcrafted Feature Learning and Convolutional Neural Networks for Facial Expression Recognition
- 用HOG/SVM、LBP/逻辑回归和轻量CNN在三数据集上对比
- CNN在复杂数据上最优，HOG在受控环境表现好，LBP始终较差
- 揭示数据复杂度对模型性能的决定性影响，适合真实场景研究者参考
面部表情识别是人机交互、心理健康监测、驾驶注意力系统和行为分析等领域的关键计算机视觉任务。尽管卷积神经网络（CNN）主导现代表情识别，但直方图梯度方向（HOG）和局部二值模式（LBP）等手工特征仍是经典基准。本研究在FER-2013、CK+和KDEF三个数据集上比较HOG+SVM、LBP+逻辑回归与轻量CNN的性能。结果表明：CNN整体表现最佳，尤其在复杂数据上；HOG在受控环境下表现优异；而LBP在所有数据集上均表现不佳。研究强调数据复杂度显著影响模型性能，强调鲁棒特征学习对真实世界表情识别的重要性。
原文摘要 · Abstract (English)
Facial expression recognition is an important computer vision task with applications in human--computer interaction, mental health monitoring, driver alert systems, and behavioral analysis. While convolutional neural networks (CNNs) dominate modern facial expression recognition, handcrafted feature descriptors such as Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) remain useful classical baselines. This study compares HOG with Support Vector Machine (SVM), LBP with Logistic Regression, and a lightweight CNN across three facial expression datasets: FER-2013, CK+, and KDEF. The results show that CNNs achieve the best overall performance, particularly on more complex data, while HOG performs strongly in controlled environments. LBP performs poorly across all datasets. The study highlights that dataset complexity significantly affects performance and that robust feature learning is essential for real-world facial expression recognition.
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