The Emotion Analysis project aims to develop a system capable of accurately detecting and classifying human emotions from facial images. The project utilizes a combination of Local Binary Patterns (LBP) and Convolutional Neural Networks (CNNs) to extract discriminative features from facial images and classify them into different emotional categories.
The primary objective of this project is to create a robust and efficient system that can analyze facial expressions and accurately identify the corresponding emotions. By leveraging LBP and CNN techniques, the system will be able to handle real-time emotion analysis tasks, enabling applications in various domains such as human-computer interaction, customer sentiment analysis, and market research.
Gather a diverse dataset of facial images representing various emotions, including happiness, sadness, anger, surprise, fear, and disgust. Ensure the dataset includes a wide range of individuals across different ages, genders, and ethnicities.
Preprocess the facial images by standardizing the size, adjusting brightness and contrast, and normalizing the pixel values. Additionally, apply face detection and alignment techniques to ensure consistent facial landmarks across images.
Extract LBP features from the preprocessed facial images. LBP is a texture descriptor that encodes local texture patterns by comparing the intensity values of pixels with their neighbors. This technique effectively captures local facial texture information that is crucial for emotion analysis.
Select the most discriminative LBP features using techniques such as Principal Component Analysis (PCA) or feature ranking algorithms. This step reduces the dimensionality of the feature space and enhances the efficiency of subsequent processing.
Train a CNN model on the selected LBP features to learn the complex patterns associated with different emotions. The CNN architecture should include convolutional layers to extract spatial features, pooling layers for spatial subsampling, and fully connected layers for classification.
Split the dataset into training and testing sets. Train the CNN model using the training set and evaluate its performance on the testing set. Utilize appropriate evaluation metrics such as accuracy, precision, recall, and F1 score to assess the model's performance.
Fine-tune the CNN model and experiment with hyperparameter tuning to improve its performance. Explore techniques such as regularization, dropout, and data augmentation to enhance the model's generalization and robustness.
Deploy the trained CNN model to perform real-time emotion analysis on live video streams or captured frames. Implement efficient techniques for face detection and tracking to extract facial regions for analysis. Apply the trained model to classify emotions in real-time, providing instantaneous results.
Develop a user-friendly interface to visualize the emotion analysis results. Display the detected emotion labels along with relevant confidence scores or probabilities. Additionally, incorporate visualization techniques such as heatmaps or emotion representations to enhance the interpretability of the system.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
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- @kylelobo - Idea & Initial work
See also the list of contributors who participated in this project.
- Hat tip to anyone whose code was used
- Inspiration
- References
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent