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

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 wit...