"It would be interesting if AI could understand people's feelings"
"It would be convenient if we could read facial expressions with a camera and automatically analyze emotions."
Have you ever felt that way?
With Python,AI that predicts "how you're feeling" from images and faces captured on cameraAnyone can make one.
Moreover, this can be achieved with just simple code, without any complex formulas or difficult settings.
In this article,From the basics of face recognition and emotion classification to how to implement itWe will explain it in an easy-to-understand manner so that even elementary school students can understand.
This content is perfect for those who want to try their hand at sentiment analysis or who want to learn how to use AI in practical ways.
- Reading emotions through facial recognition [Image emotion reading AI]
- Libraries to be used and preparation steps [Python emotion recognition library]
- Reading emotions with actual code [Python face recognition emotion analysis]
- Real-time analysis of camera footage [Python emotion camera analysis]
- Record emotion log [Python emotion log record CSV]
- Use cases in real life and education [Emotion AI Education Business Use]
- Summary: The appeal of creating emotional AI with Python [Python Emotion Recognition Summary]
Reading emotions through facial recognition [Image emotion reading AI]
conclusion
In Python,AI that classifies emotions from facial expressionsYou can create
It can detect facial expressions such as smiles, anger, sadness, and surprise to determine emotions.
reason
Facial expressions are a way of communicating emotions without saying words.
For example, turning up the corners of the mouth means "happy" and lowering the eyebrows means "sad."
If we could read these facial expressions as data,Emotions can be treated as numbersIt will look like this.
Examples
• Analyze students' responses during class to estimate their level of understanding
• Analyze the emotions of customers in your store and use them to improve customer service
• Automatically record facial expressions during Zoom meetings to visualize the team atmosphere
summary
Emotion recognition AI is a technology that makes invisible "feelings" visibleis.
With Python, you can develop without any specialized knowledge.
Libraries to be used and preparation steps [Python emotion recognition library]
conclusion
Python has a number of useful libraries for image processing and sentiment analysis.
reason
It's hard to create an AI from scratch,Already trained modelBy utilizing this, anyone can easily use it.
Using the following libraries, you can analyze sentiment with just a few lines of code:
Examples
The libraries and installation commands used are as follows.
1 | |
• OpenCV: Used for acquiring camera images and face detection
• DeepFace: AI library for classifying emotions from images (pre-trained)
summary
Using the Python library,It is possible to read emotions without knowing the complicated mechanics of AIis.
Reading emotions with actual code [Python face recognition emotion analysis]
conclusion
It is possible to determine emotions from just one image.
reason
The DeepFace library automatically detects faces in an image and classifies emotions such as "happiness" or "anger" using a pre-trained model.
Examples
1 | = ==[]) 0][]) |
Example output:
1 | |
In this way,Probability is output for each emotionwill be done.
summary
Sentiment analysis is possible with just one imageIt's the era we live in. You can also use photos taken with a camera, which makes it easy to experiment with.
Real-time analysis of camera footage [Python emotion camera analysis]
conclusion
Python can also read emotions from camera footage in real time.
reason
You can capture images from a webcam using OpenCV.
If you send that video to DeepFace,Emotion estimation in real timeIt will be.
Examples
1 | = 0) = = =[== 0][] (: (1) & == (): |
When you run it,A window that displays current emotions in real timewill open.
summary
Real-time emotion determinationInteractive AI ExperienceIt is ideal for home, education and research.
Record emotion log [Python emotion log record CSV]
conclusion
If you save your emotion history in a CSV file, you can later create graphs or perform statistical analysis.
reason
Instead of focusing on one-off emotions, think about "what feelings did you have most often in the past week?"Viewing in chronological orderis important.
Examples
1 | (, =%-%-%%%% |
If you incorporate this code into real-time judgment,Emotion history is automatically accumulatedwill be done.
summary
Keeping a record of your emotionsVisualizing sentiment trends using AIThis allows for deeper analysis.
Use cases in real life and education [Emotion AI Education Business Use]
conclusion
AI that can read emotionsActive in business and education.
reason
Being able to see emotions changes the way you interact with people and makes you kinder to them.
Examples
• Parents check their children's emotional changes every day
• Salespeople record the reactions of their counterparts during sales negotiations
• AI teaching materials used to analyze comprehension in online learning
Looking at sentiment trends,Visualize changes in your mind and stresscan.
summary
Emotion recognition AINot just technology, but training wheels for human understandingIt is very useful as.
Summary: The appeal of creating emotional AI with Python [Python Emotion Recognition Summary]
• With Python,AI that reads emotions from facial imagesIt's easy to make
• Libraries such as DeepFaceGet started with just a few lines of code
• It also has many practical applications, such as camera integration, CSV recording, and graphing.
• In education and business,Technology that is close to people's heartsIt can be used as
Please take this opportunity toCreate your own emotional AI with Python.
It will make your daily life a little more fun and easier.
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