metadata
language: en
tags:
- facial-emotion-recognition
- computer-vision
- tensorflow
- keras
license: apache-2.0
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