An Empirical Study of Handcrafted Feature Learning and Convolutional Neural Networks for Facial Expression Recognition

A new study comparing handcrafted feature descriptors and convolutional neural networks (CNNs) for facial expression recognition finds that CNNs achieve the best overall performance across three datasets (FER-2013, CK+, KDEF), particularly on complex data, while Histogram of Oriented Gradients (HOG) performs strongly in controlled environments and Local Binary Patterns (LBP) performs poorly across all datasets. The research, published on arXiv, underscores that dataset complexity significantly affects performance and that robust feature learning is essential for real-world applications.

arXiv:2607.15288v1 Announce Type: new Abstract: 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.