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

• URL: http://arxiv.org/abs/2607.15288v1
• Date: Fri, 19 Jun 2026 02:01:11 GMT
• Title: An Empirical Study of Handcrafted Feature Learning and Convolutional Neural Networks for Facial Expression Recognition
• Abstract summary: Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) remain useful classical baselines.<n>This study compares HOG with Support Vector Machine (SVM), LBP with Logistic Regression, and a lightweight CNN across three facial expression datasets.<n>CNNs achieve the best overall performance, particularly on more complex data, while HOG performs strongly in controlled environments.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Deep Learning-Based Real-Time Sequential Facial Expression Analysis Using Geometric Features [1.0742675209112622]
  This study presents a novel approach to real-time sequential facial expression recognition using deep learning and geometric features.<n>The proposed method utilizes MediaPipe FaceMesh for rapid and accurate facial landmark detection.<n>The approach demonstrated real-time applicability, processing approximately 165 frames per second on consumer-grade hardware.
  arXiv  Detail & Related papers  (2025-12-05T12:26:31Z)
• Alleviating Catastrophic Forgetting in Facial Expression Recognition with Emotion-Centered Models [49.3179290313959]
  The proposed method, emotion-centered generative replay (ECgr), tackles this challenge by integrating synthetic images from generative adversarial networks. ECgr incorporates a quality assurance algorithm to ensure the fidelity of generated images. The experimental results on four diverse facial expression datasets demonstrate that incorporating images generated by our pseudo-rehearsal method enhances training on the targeted dataset and the source dataset.
  arXiv  Detail & Related papers  (2024-04-18T15:28:34Z)
• Joint-Embedding Masked Autoencoder for Self-supervised Learning of Dynamic Functional Connectivity from the Human Brain [16.62883475350025]
  Graph Neural Networks (GNNs) have shown promise in learning dynamic functional connectivity for distinguishing phenotypes from human brain networks.<n>We introduce the Spatio-Temporal Joint Embedding Masked Autoencoder (ST-JEMA), drawing inspiration from the Joint Embedding Predictive Architecture (JEPA) in computer vision.
  arXiv  Detail & Related papers  (2024-03-11T04:49:41Z)
• A Hybrid End-to-End Spatio-Temporal Attention Neural Network with Graph-Smooth Signals for EEG Emotion Recognition [1.6328866317851187]
  We introduce a deep neural network that acquires interpretable representations by a hybrid structure of network-temporal encoding and recurrent attention blocks. We demonstrate that our proposed architecture exceeds state-of-the-art results for emotion classification on the publicly available DEAP dataset.
  arXiv  Detail & Related papers  (2023-07-06T15:35:14Z)
• Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer Perceptrons [50.33260238739837]
  Graph networks (GNNs) have demonstrated remarkable capabilities in learning from graph-structured data.<n>There remains a lack of analysis comparing GNNs and generalizations from an optimization and generalization perspective.
  arXiv  Detail & Related papers  (2023-06-24T10:21:11Z)
• Language Knowledge-Assisted Representation Learning for Skeleton-Based Action Recognition [71.35205097460124]
  How humans understand and recognize the actions of others is a complex neuroscientific problem. LA-GCN proposes a graph convolution network using large-scale language models (LLM) knowledge assistance.
  arXiv  Detail & Related papers  (2023-05-21T08:29:16Z)
• Neural Architecture Search Using Genetic Algorithm for Facial Expression Recognition [2.7504274245107303]
  We propose a genetic algorithm that uses an ingenious encoding-decoding mechanism that allows to automatically evolve CNNs on FER tasks. The proposed algorithm achieves the best-known results on the CK+ and FERG datasets as well as competitive results on the JAFFE dataset.
  arXiv  Detail & Related papers  (2023-04-12T16:36:07Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• Progressive Spatio-Temporal Bilinear Network with Monte Carlo Dropout for Landmark-based Facial Expression Recognition with Uncertainty Estimation [93.73198973454944]
  The performance of our method is evaluated on three widely used datasets. It is comparable to that of video-based state-of-the-art methods while it has much less complexity.
  arXiv  Detail & Related papers  (2021-06-08T13:40:30Z)
• Facial expression and attributes recognition based on multi-task learning of lightweight neural networks [9.162936410696409]
  We examine the multi-task training of lightweight convolutional neural networks for face identification and classification of facial attributes. It is shown that it is still necessary to fine-tune these networks in order to predict facial expressions. Several models are presented based on MobileNet, EfficientNet and RexNet architectures.
  arXiv  Detail & Related papers  (2021-03-31T14:21:04Z)
• Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks [82.54695985117783]
  We investigate the suitability of state-of-the-art deep learning architectures for continuous emotion recognition using long video sequences captured in-the-wild. We have developed and evaluated convolutional recurrent neural networks combining 2D-CNNs and long short term-memory units, and inflated 3D-CNN models, which are built by inflating the weights of a pre-trained 2D-CNN model during fine-tuning.
  arXiv  Detail & Related papers  (2020-11-18T13:42:05Z)
• The FaceChannel: A Fast & Furious Deep Neural Network for Facial Expression Recognition [71.24825724518847]
  Current state-of-the-art models for automatic Facial Expression Recognition (FER) are based on very deep neural networks that are effective but rather expensive to train. We formalize the FaceChannel, a light-weight neural network that has much fewer parameters than common deep neural networks. We demonstrate how our model achieves a comparable, if not better, performance to the current state-of-the-art in FER.
  arXiv  Detail & Related papers  (2020-09-15T09:25:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.