# Hy-Facial: Hybrid Feature Extraction by Dimensionality Reduction Methods for Enhanced Facial Expression Classification

arXiv: https://arxiv.org/abs/2509.26614 · HTML: https://mlsanity.com/paper/2509.26614/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 50 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Builds on (most cited first, 20 of 20)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving](https://mlsanity.com/paper/2505.17685/) (2025)
- [Machine Learning for Synthetic Data Generation: A Review](https://mlsanity.com/paper/2302.04062/) (2023)
- [FastPillars: A Deployment-friendly Pillar-based 3D Detector](https://mlsanity.com/paper/2302.02367/) (2023)
- [DeDoDe v2: Analyzing and Improving the DeDoDe Keypoint Detector](https://mlsanity.com/paper/2404.08928/) (2024)
- [RegFormer: An Efficient Projection-Aware Transformer Network for Large-Scale Point Cloud Registration](https://mlsanity.com/paper/2303.12384/) (2023)
- [M2IV: Towards Efficient and Fine-grained Multimodal In-Context Learning via Representation Engineering](https://mlsanity.com/paper/2504.04633/) (2025)
- [BALF: Simple and Efficient Blur Aware Local Feature Detector](https://mlsanity.com/paper/2211.14731/) (2022)
- [TWIN-GPT: Digital Twins for Clinical Trials via Large Language Model](https://mlsanity.com/paper/2404.01273/) (2024)
- [Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction](https://mlsanity.com/paper/2309.00783/) (2023)
- [CATP: Contextually Adaptive Token Pruning for Efficient and Enhanced Multimodal In-Context Learning](https://mlsanity.com/paper/2508.07871/) (2025)
- [YOLO-PPA based Efficient Traffic Sign Detection for Cruise Control in Autonomous Driving](https://mlsanity.com/paper/2409.03320/) (2024)
- [ICH-SCNet: Intracerebral Hemorrhage Segmentation and Prognosis Classification Network Using CLIP-guided SAM mechanism](https://mlsanity.com/paper/2411.04656/) (2024)
- [Gated Multimodal Graph Learning for Personalized Recommendation](https://mlsanity.com/paper/2506.00107/) (2025)
- [Information Entropy Guided Height-aware Histogram for Quantization-friendly Pillar Feature Encoder](https://mlsanity.com/paper/2405.18734/) (2024)
- [Sensitivity-LoRA: Low-Load Sensitivity-Based Fine-Tuning for Large Language Models](https://mlsanity.com/paper/2509.09119/) (2025)
- [CRISP-SAM2: SAM2 with Cross-Modal Interaction and Semantic Prompting for Multi-Organ Segmentation](https://mlsanity.com/paper/2506.23121/) (2025)
- [Governance-Ready Small Language Models for Medical Imaging: Prompting, Abstention, and PACS Integration](https://mlsanity.com/paper/2508.13378/) (2025)
- [Image Matching Filtering and Refinement by Planes and Beyond](https://mlsanity.com/paper/2411.09484/) (2024)

## Built on by (most cited first, 10 of 10)

- [HUD: Hierarchical Uncertainty-Aware Disambiguation Network for Composed Video Retrieval](https://mlsanity.com/paper/2512.02792/) (2025)
- [Does Tone Change the Answer? Evaluating Prompt Politeness Effects on Modern LLMs: GPT, Gemini, and LLaMA](https://mlsanity.com/paper/2512.12812/) (2025)
- [Cornerstones or Stumbling Blocks? Deciphering the Rock Tokens in On-Policy Distillation](https://mlsanity.com/paper/2605.09253/) (2026)
- [MUSE: Resolving Manifold Misalignment in Visual Tokenization via Topological Orthogonality](https://mlsanity.com/paper/2605.05646/) (2026)
- [Parametric Memory Decoding for Zero-Shot Routing in LoRA-Based External Parametric Memory](https://mlsanity.com/paper/2607.04118/) (2026)
- [Manifold Dimension Estimation via Local Graph Structure](https://mlsanity.com/paper/2510.15141/) (2025)
- [Hierarchical Retrieval Augmented Generation for Adversarial Technique Annotation in Cyber Threat Intelligence Text](https://mlsanity.com/paper/2604.14166/) (2026)
- [Avoiding Overthinking and Underthinking: Curriculum-Aware Budget Scheduling for LLMs](https://mlsanity.com/paper/2604.19780/) (2026)
- [MechELK: A Mechanistic Interpretability Framework for Eliciting Latent Knowledge in Large Language Models](https://mlsanity.com/paper/2605.28825/) (2026)
- [Estimating Treatment Effects for Depression in Longitudinal Therapy Switching Settings](https://mlsanity.com/paper/2607.27214/) (2026)

## Abstract (arXiv)

Facial expression classification remains a challenging task due to the high dimensionality and inherent complexity of facial image data. This paper presents Hy-Facial, a hybrid feature extraction framework that integrates both deep learning and traditional image processing techniques, complemented by a systematic investigation of dimensionality reduction strategies. The proposed method fuses deep features extracted from the Visual Geometry Group 19-layer network (VGG19) with handcrafted local descriptors and the scale-invariant feature transform (SIFT) and Oriented FAST and Rotated BRIEF (ORB) algorithms, to obtain rich and diverse image representations. To mitigate feature redundancy and reduce computational complexity, we conduct a comprehensive evaluation of dimensionality reduction techniques and feature extraction. Among these, UMAP is identified as the most effective, preserving both local and global structures of the high-dimensional feature space. The Hy-Facial pipeline integrated VGG19, SIFT, and ORB for feature extraction, followed by K-means clustering and UMAP for dimensionality reduction, resulting in a classification accuracy of 83. 3% in the facial expression recognition (FER) dataset. These findings underscore the pivotal role of dimensionality reduction not only as a pre-processing step but as an essential component in improving feature quality and overall classification performance.