Facial_Emotion_Detection

Facial Emotion Detection Model

A lightweight deep learning model that classifies facial expressions into 7 emotion categories.

Model Details

• Model type: Image Classification
• Architecture: ResNet50-based
• Input: 224x224 RGB images
• Output: 7 emotion classes
• Accuracy: 85.60%

Emotion Classes

• 😠 Angry
• 🤢 Disgust
• 😨 Fear
• 😊 Happy
• 😐 Neutral
• 😢 Sad
• 😲 Surprise

Quick Start

from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np

# Load model
model = load_model('Facial_Emotion_Detection_Model.h5')

# Preprocess image
img = Image.open('face.jpg').convert('RGB').resize((224, 224))
x = np.array(img) / 255.0
x = np.expand_dims(x, axis=0)

# Predict
predictions = model.predict(x)
emotion = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise'][np.argmax(predictions)]
confidence = np.max(predictions)

print(f"Emotion: {emotion} ({confidence:.2%})")
Usage
Ideal for:

Emotion analysis applications

Human-computer interaction

Customer sentiment analysis

Research projects

Limitations
Best with frontal face images

Performance varies with image quality

Cultural differences may affect accuracy

License: Apache 2.0