Project Background:
Multimodal emotion recognition stems from the growing recognition of the importance of understanding human emotions for enhanced human-computer interaction and emotional intelligence in AI systems. Emotions are expressed through facial expressions, speech patterns, and gestures, creating a rich spectrum of emotional cues. This work aims to capitalize on these diverse cues by developing advanced multimodal emotion recognition systems. Traditional unimodal approaches often fall short of capturing the intricacies of human emotions due to the inherent multimodal nature of emotional expression. This project addresses the limitation by integrating computer vision for facial expression analysis, natural language processing for sentiment analysis in speech and text, and other sensor-based modalities. The project aims to create robust models capable of accurately recognizing and interpreting complex emotional states by combining information from multiple channels. Additionally, the project considers the challenges of cross-modal fusion, ensuring that the combined information leads to a holistic and accurate representation of an individuals emotional state.
Problem Statement
- This work addresses challenges such as integrating information from different modalities by ensuring synchronization and coherence across diverse emotional cues.
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Additionally, it seeks to overcome the limitations posed by the subjectivity and context-dependency of emotions, requiring robust models capable of generalizing across diverse individuals and scenarios.
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It also delves into the intricacies of cross-modal fusion, where the challenge lies in effectively combining and interpreting information from various sensory inputs.
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Ultimately, the goal is to develop advanced systems that can accurately decipher and respond to human emotions, fostering more natural and empathetic interactions between humans and machines.
Aim and Objectives
- Enhance human-computer interaction and emotional intelligence in AI systems by accurately recognizing human emotions across multiple modalities.
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Develop robust models for accurate facial expression analysis in emotion recognition.
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Implement natural language processing techniques for sentiment analysis in speech and text.
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Explore sensor-based modalities for additional cues in emotion recognition.
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Investigate cross-modal fusion strategies to integrate information from diverse sources.
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Address challenges of subjectivity and context-dependency in emotion recognition.
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Ensure generalizability across diverse individuals and real-world scenarios.
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Consider ethical implications and privacy concerns in multimodal emotion data collection and processing.
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Foster the creation of empathetic and context-aware AI systems that respond to human emotions appropriately.
Contributions to Multimodal Emotion Recognition
1. The development of innovative approaches for seamlessly integrating and interpreting emotional cues from facial expressions, speech, and additional sensor-based modalities.
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Contributing to the field by exploring and implementing effective strategies for cross-modal fusion, ensuring a holistic representation of emotional states.
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Improving the accuracy of facial expression analysis in emotion recognition models, capturing subtle changes in emotional expressions.
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Integrating natural language processing techniques for more accurate sentiment analysis in speech and text, enriching the understanding of emotional context.
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Addressing challenges related to subjectivity and context-dependency, enhancing the generalizability of emotion recognition models across diverse individuals and real-world scenarios.
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Incorporating ethical considerations like privacy concerns in collecting and processing multimodal emotion data, ensuring responsible and transparent practices.
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Focussing on fostering the development of empathetic and context-aware AI systems, improving human-computer interactions by accurately responding to a wide range of human emotions.
Deep Learning Algorithms for Multimodal Emotion Recognition
- Multimodal Neural Networks (MNN)
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Convolutional Neural Networks for Facial Expression Analysis (Facial CNNs)
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Long Short-Term Memory Networks for Speech and Text Analysis
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Attention-based Multimodal Fusion Models
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Graph Neural Networks for Multimodal Emotion Recognition
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Capsule Networks for Capturing Hierarchical Relationships in Modalities
Datasets for Multimodal Emotion Recognition
- AffectNet - Facial Expression Recognition in the Wild
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EMOReact - Multimodal Emotion Recognition in Response to Videos
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AVEC (Audio/Visual Emotion Challenge)
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DEAP (Database for Emotion Analysis using Physiological Signals)
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SEMAINE - Multimodal Sentiment Analysis in Real-life Conversations
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EMOTIC - Emotion Recognition in Images with Textual Descriptions
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Friends TV Show Dataset - Multimodal Emotion Recognition in Video Clips
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CMU-MOSEI - Multimodal Sentiment Analysis Dataset
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RAVDESS (Ryerson Audio-Visual Database of Emotional Speech and Song)
Performance Metrics
- Accuracy
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Precision
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Recall
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F1 Score
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Area Under the Receiver Operating Characteristic Curve (AUC-ROC)
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Matthews Correlation Coefficient (MCC)
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Mean Squared Error (MSE)
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Concordance Correlation Coefficient (CCC)
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Spearmans Rank Correlation
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Kullback-Leibler Divergence (KL Divergence)
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Root Mean Squared Error (RMSE)
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Hamming Loss
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Jaccard Similarity Coefficient
Software Tools and Technologies
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1. Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2. Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
