Facial Recognition for Emotion Detection

Developing a real-time emotion recognition system with high accuracy across diverse demographic profiles.

Problem Statement

Develop a real-time emotion recognition system with high accuracy across diverse demographic profiles.

Understanding the complexities of human emotions

Technical Implementation

Data Preparation: Collected and preprocessed over 100,000 images from FER-2013 and AffectNet datasets, ensuring balanced representation across emotion classes.
Model Architecture: Utilized a Convolutional Neural Network (CNN) based on the ResNet-50 architecture, fine-tuned for multi-class emotion classification.
Real-Time Processing: Integrated OpenCV for facial detection and implemented pre-processing steps such as alignment and normalization to improve model accuracy.
Deployment: Deployed the model using ONNX runtime to optimize inference on edge devices, achieving real-time processing at 30 FPS.

Figure 1: CNN Model Architecture for Emotion Detection

Results

92%

Classification Accuracy

50ms

Latency per Frame

30 FPS

Real-Time Processing

Achieved a 92% accuracy in classifying emotions across seven categories: happiness, sadness, anger, fear, surprise, disgust, and neutrality.
Reduced latency to under 50 milliseconds per frame on consumer-grade GPUs.
Successfully integrated into customer service applications, enhancing user experience through emotion-adaptive responses.

Figure 2: Accuracy Comparison Across Emotion Classes

"The emotion recognition system developed by MeldaTech has revolutionized our customer interactions. The real-time insights into customer emotions have significantly enhanced our service quality."
— Jane Doe, CTO at CustomerServiceCo