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tharinda-pamindu/
Facial-Emotion-Detection

Name: Facial-Emotion-Detection
Availability: InStock

keras

image-classification

facial-expression-recognition

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Facial Emotion Detection System (Inflators)

This model is a Convolutional Neural Network (CNN) trained to detect facial emotions from images. It classifies faces into one of 6 categories: Happiness, Sadness, Anger, Surprise, Fear, and Neutral.

This project was developed by the Inflators group for the ICT3212 - Introduction to Intelligent Systems module at Rajarata University of Sri Lanka.

Model Description

Model Type: Convolutional Neural Network (CNN)
Framework: TensorFlow / Keras
Input: 48x48 pixel grayscale images
Output: 6 emotion classes (Softmax probability distribution)
Training Dataset: FER-2013

Intended Use

This model is designed for:

Real-time facial emotion detection via webcam.
Analyzing video streams for emotional content.
psychological research or customer satisfaction monitoring (experimental use).

It is not intended for high-stakes decision-making or surveillance without human oversight.

Performance

The model aims to achieve an accuracy of >60% on the FER-2013 test set, which is a competitive baseline for this difficult dataset given the 6-class problem (random guessing would be ~16%).

Limitations

Occlusions: Performance may degrade if the face is partially covered (masks, glasses, hands).
Lighting: Extreme lighting conditions (too dark/too bright) can affect detection accuracy.
Head Pose: The model works best on frontal faces; significant head rotation may lead to misclassification.
Data Bias: The FER-2013 dataset has class imbalances and some incorrectly labeled images, which may bias the model's predictions.

How to Use

1. Installation

Ensure you have the necessary libraries installed:

pip install tensorflow numpy opencv-python

2. Loading the Model

python

import tensorflow as tf
import cv2
import numpy as np

# Load the model
model = tf.keras.models.load_model('emotion_model.keras')

# Define emotion labels
emotion_labels = {0: 'Anger', 1: 'Fear', 2: 'Happy', 3: 'Sad', 4: 'Surprise', 5: 'Neutral'}

3. Inference on an Image