arxiv_cv 90% Match Research Paper AI Researchers in Affective Computing,Developers of HCI applications,Computer Vision Engineers 1 month ago

InsideOut: An EfficientNetV2-S Based Deep Learning Framework for Robust Multi-Class Facial Emotion Recognition

computer-vision › scene-understanding

📄 Abstract

Abstract: Facial Emotion Recognition (FER) is a key task in affective computing, enabling applications in human-computer interaction, e-learning, healthcare, and safety systems. Despite advances in deep learning, FER remains challenging due to occlusions, illumination and pose variations, subtle intra-class differences, and dataset imbalance that hinders recognition of minority emotions. We present InsideOut, a reproducible FER framework built on EfficientNetV2-S with transfer learning, strong data augmentation, and imbalance-aware optimization. The approach standardizes FER2013 images, applies stratified splitting and augmentation, and fine-tunes a lightweight classification head with class-weighted loss to address skewed distributions. InsideOut achieves 62.8% accuracy with a macro averaged F1 of 0.590 on FER2013, showing competitive results compared to conventional CNN baselines. The novelty lies in demonstrating that efficient architectures, combined with tailored imbalance handling, can provide practical, transparent, and reproducible FER solutions.

Key Contributions

InsideOut presents a reproducible FER framework using EfficientNetV2-S, transfer learning, and tailored imbalance handling. It standardizes images, applies augmentation, and uses class-weighted loss to address dataset imbalance, achieving competitive results on FER2013.

Business Value

Enables more accurate and reliable emotion recognition systems, crucial for applications like personalized learning platforms, mental health monitoring, and improving user experience in interactive systems.

Paper Metadata

Innovation Type

Framework and Optimization Strategy

Deployment Feasibility

High. The use of EfficientNetV2-S suggests a focus on efficiency, making it suitable for various deployment scenarios, including potentially edge devices with sufficient processing power.

Limitations Addressed

Occlusions, illumination, and pose variations in facial images,Subtle intra-class differences,Dataset imbalance hindering recognition of minority emotions

Performance Gains

62.8% accuracy on FER2013,0.590 macro averaged F1 on FER2013,Competitive results compared to conventional CNN baselines

Technical Tags

Facial Emotion Recognition (FER)Affective ComputingEfficientNetV2-STransfer LearningData AugmentationImbalance-Aware OptimizationClass-Weighted LossFER2013 datasetDeep LearningCNN

Research Topics

Emotion RecognitionComputer VisionDeep LearningHuman-Computer InteractionRobustness

Methods & Architectures

Transfer learningStrong data augmentationImbalance-aware optimizationClass-weighted lossStandardizationStratified splitting EfficientNetV2-SLightweight classification head

Applications & Tasks

Human-Computer Interaction (HCI) E-learning Healthcare Safety Systems Market Research Facial Emotion RecognitionHandling OcclusionsIllumination VariationsPose VariationsDataset ImbalanceSubtle Intra-class Differences Multi-class Facial Emotion Recognition

Datasets & Benchmarks

Datasets

FER2013

Benchmarks

FER2013 dataset

AccuracyMacro averaged F1 score

Related Fields

Computer VisionAffective ComputingDeep LearningHuman-Computer Interaction

Keywords

Facial Emotion RecognitionFERAffective ComputingEfficientNetV2-STransfer LearningData AugmentationDataset ImbalanceClass-Weighted LossFER2013Deep LearningCNN

Academic Context

#Emotion Recognition#Computer Vision#Deep Learning#Human-Computer Interaction#Robustness

Technology Stack

Frameworks & Libraries

EfficientNetV2-S

Commercial Potential

Potential Products

Emotion-aware user interfacesTools for analyzing customer sentiment from videoMental health monitoring applications

Target Industries

TechnologyHealthcareEducationMarketingAutomotive (driver monitoring)

Use Case Examples

Adapting educational content based on student engagementMonitoring driver drowsiness or distractionAnalyzing customer reactions to products in real-time

Competitive Edge

Provides a robust and reproducible framework that specifically tackles the challenges of dataset imbalance and variations in facial images, aiming for better performance than generic FER models.

Market Opportunity

Growing market for affective computing and AI-driven user experience.

Revenue Models

Licensing of the FER model/frameworkintegration into software products.

Resource Requirements

Compute Needs

Moderate, suitable for training on standard GPU setups.

Data Requirements

Labeled facial images with corresponding emotion labels (e.g., FER2013).

Deployment Constraints

Performance can degrade with extreme variations in pose, illumination, or occlusion not well-represented in the training data. Real-time performance requirements.

Scalability

The framework's scalability depends on the EfficientNetV2-S architecture and the efficiency of the data augmentation and loss functions.

Regulatory Considerations

Ethical considerations regarding emotion recognitiondata privacy (GDPRCCPA).

Production Readiness

Maturity Level

Research

Time to Market

1-3 years

Patent Potential

Low, as it's a framework combining existing techniques.

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