Project Title: Emotion Detection from Facial Expressions Using Deep Learning

Objective:

To develop a model that can automatically detect human emotions (e.g., happy, sad, angry) from facial expressions in images or video frames.

Dataset:

FER-2013: A popular dataset consisting of 35,887 grayscale 48x48 pixel face images categorized into 7 emotion classes:

Classes: Angry, Disgust, Fear, Happy, Sad, Surprise, Neutral

Key Steps:

Data Preprocessing:

Normalize pixel values.

Resize images (if using a custom image source).

Convert labels to categorical format.

Augment data to improve robustness (rotation, zoom, etc.).

Model Building:

Use Convolutional Neural Networks (CNNs) for feature extraction and classification.

Advanced approaches may include transfer learning with pre-trained models like VGG, ResNet.

Training & Validation:

Split dataset into training and validation sets.

Use techniques like dropout, batch normalization to prevent overfitting.

Evaluation:

Accuracy, confusion matrix, precision/recall per emotion class.

Model performance may vary across emotion types (e.g., Disgust is often harder to classify accurately).

Deployment:

Integrate into real-time applications using OpenCV for face detection and live video emotion classification.

Tools & Libraries:

Python, OpenCV, NumPy

TensorFlow/Keras or PyTorch

Matplotlib/Seaborn for visualization

Applications:

Human-computer interaction

Customer feedback analysis

Mental health monitoring

Security and surveillance