Project Working of Emotion and Stress Level Recognition:-
This project is based on python programming language and it will recognize the person emotion and model will identify the stress level. Once user run the python script from terminal or code editor then laptop front camera will open and it will identify person emotion like sad, happy, angry, etc. With addition to the emotion it will recognize human stress level, the model will identify person eyebrow.
In this project, we’re making a system with Python to understand how people feel and if they’re stressed. Python helps us do many things easily. We’ll use it to look at faces and bodies to see emotions and stress. We’ll teach the computer to learn from examples and guess feelings. The system will work fast, so we can use it in hospitals and with computers. This helps us understand people better and help them when they’re stressed or feeling down. This Machine Learning model is trained on large dataset of person emotion from a training script and once model got trained then user can test from testing python script.
Stress Detection using Machine Learning | Emotion Detection using CNN
-Goal: Make a Python system that understands feelings and stress.
– Tools: Use Python’s tricks to look at faces and body signals.
– Faces: See if someone is happy, sad, angry, or surprised by looking at their face.
– **Body Signals**: Check heart rate and skin to know if someone is stressed.
– **Learning**: Teach the computer to learn from examples and guess feelings.
– **Live Analysis**: Make it work fast to see how people feel in real-time.
– **Usefulness**: Helps doctors, computers, and psychologists understand people better.
Project Working of Emotion and Stress Level Recognition:-
This project is based on python programming language and it will recognise the person emotion and model will identify the stress level. Once user run the python script from terminal or code editor then laptop front camera will open and it will identify person emotion like sad, happy, angry, etc. With addition to the emotion it will recognise human stress level, the model will identify person eyebrow.
An EMOTION DETECTION AND RECOGNITION SYSTEM (EDRS) is an artificial intelligence-based technology that aims to identify and interpret human emotions from various sources, such as text, speech, images, or videos. The goal of such a system is to understand the emotional state of a person, enabling it to provide personalized and context-aware responses or actions.
An EMOTION DETECTION AND RECOGNITION SYSTEM is a technology that aims to identify and interpret human emotions based on various cues, such as facial expressions, voice tone, body language, and physiological signals. It utilizes machine learning and artificial intelligence techniques to analyze these cues and classify them into different emotional states.
Here are key components of the working of the EMOTION DETECTION AND RECOGNITION SYSTEM:–
- 1.Data Collection
- 2.Facial Expression Analysis
- 3.Voice Analysis
- 4.Body Language and Gesture Analysis
- 5.Physiological Signals
- 6.Machine Learning Algorithms
Outline of the EMOTION DETECTION AND RECOGNITION SYSTEM process:–
1.Data Collection: Gather a dataset containing emotional expressions with corresponding labels. You can use publicly available datasets or collect your own data.
2.Preprocessing: Preprocess the data to standardize and prepare it for training. For facial expression analysis, this may involve face detection and alignment. For voice analysis, it may involve audio feature extraction.
3.Feature Extraction: Extract relevant features from the preprocessed data. For facial expression analysis, this could be facial landmarks or deep learning-based features. For voice analysis, features like Mel-frequency cepstral coefficients (MFCCs) are commonly used.
4.Machine Learning Model Selection: Choose a suitable machine learning model or algorithm for the emotion recognition task. Common choices include Support Vector Machines (SVM), Random Forest, or deep learning-based models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
5.Model Training: Split the dataset into training and testing sets. Train the selected model on the training data.
6.Model Evaluation: Evaluate the trained model on the testing data to assess its performance and accuracy.
7.Real-time Emotion Detection (Optional): Implement a real-time emotion detection system by integrating the trained model with a camera or microphone input.
CONCLUSION
The EMOTION DETECTION AND RECOGNITION SYSTEM can improve human-computer interaction by enabling machines to understand and respond to users’ emotional states.
Tensorflow is an open-source machine learning library developed by Google. It is one of the most widely used and popular deep learning frameworks in the field of artificial intelligence. TensorFlow provides a versatile platform for building, training, and deploying various machine learning models, with a strong focus on deep learning algorithms. TensorFlow is a powerful and flexible machine learning library that enables the development of sophisticated machine learning models, with a strong focus on deep learning, while providing accessibility to both beginners and advanced users alike. Its wide range of functionalities and extensive community support make it a leading choice for various AI projects and research.
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
- Input Devices : Keyboard, Mouse.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- RAM : 4 GB.
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