手工特征学习与卷积神经网络在面部表情识别中的实证研究
An Empirical Study of Handcrafted Feature Learning and Convolutional Neural Networks for Facial Expression Recognition
浏览论文内容
中文总结 AI 辅助
该研究在三个面部表情数据集上比较HOG、LBP与轻量级CNN用于表情识别，结果显示CNN总体性能最佳，HOG在受控环境表现好，LBP较差，强调数据集复杂性影响性能，稳健特征学习对现实面部表情识别很关键。
中文摘要 AI 辅助
面部表情识别是计算机视觉中的重要任务，应用于人机交互、心理健康监测等领域。虽然卷积神经网络主导现代面部表情识别，但手工特征描述符如HOG和LBP仍是有用的经典基线。本研究在FER-2013、CK+和KDEF三个面部表情数据集上比较了HOG与支持向量机、LBP与逻辑回归以及轻量级CNN。结果表明，CNN总体性能最佳，HOG在受控环境下表现出色，LBP在所有数据集上表现不佳。研究强调数据集复杂性显著影响性能，稳健的特征学习对现实世界面部表情识别至关重要。
英文摘要
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.
