What is the application principle of AI emotion and sentiment analysis in computer vision?

Emotional artificial intelligence, also known as affective computing, is a computer vision analysis technology that analyzes personal emotions and emotional states through facial emotion detection and visual data emotion assessment. It helps us understand human emotions and has applications in various fields such as mental health, market research, and education.

Visual emotion analysis (VEA) is a challenging task that aims to bridge the emotional gap between low-level pixels and high-level emotions. Despite many difficulties, visual sentiment analysis holds great potential, as understanding human emotions is crucial to achieving powerful artificial intelligence. In recent years, the rapid development of convolutional neural networks (CNN) has made deep learning a new choice for sentiment analysis. Through CNN, we can leverage its advanced feature extraction capabilities and adaptive learning capabilities to capture emotional information in images. This approach is expected to improve the accuracy and efficiency of sentiment analysis and lay the foundation for smarter computer vision systems. Although current challenges still exist, visual emotion analysis will become an important research direction in the field of computer vision in the near future.

How Artificial Intelligence Sentiment Analysis Works

An artificial intelligence emotion application or vision system consists of the following steps:

1. Obtain image frames from the camera source;

2. Image preprocessing, cropping, resizing, rotation, color correction;

3. Use CNN model to extract important features;

4. Perform emotion classification.

Basics of AI emotion recognition

Face detection in images and videos

Usage Camera or video footage to detect and locate faces. Bounding box coordinates are used to indicate the exact face location in real time. The face detection task remains challenging, and detection of all faces in a given input image is not guaranteed, especially in uncontrolled environments with challenging lighting conditions, different head poses, long distances, or occlusions .

Image preprocessing

When a face is detected, the image data is optimized before being fed into the emotion classifier. This step greatly improves detection accuracy. Image preprocessing usually includes multiple sub-steps to normalize illumination changes, noise reduction, image smoothing, image rotation correction, image resizing and image cropping.

AI Model Emotion Classification

After preprocessing, the relevant features are retrieved from the preprocessed data containing the detected faces. There are different ways to detect many facial features. For example, action units (AU), movement of facial feature points, distance between facial feature points, gradient features, facial texture, etc.

Typically, classifiers used for AI emotion recognition are based on support vector machines (SVM) or convolutional neural networks (CNN). Finally, the recognized faces are classified based on their facial expressions by assigning predefined categories.

What emotions can computer vision detect?

The emotions or emotional expressions that an AI model can detect depend on the category it was trained on. Most emotion databases have the following emotions:

• Anger
• Disgust
• Fear
• Happiness
• Sadness
• surprise

The above is the detailed content of What is the application principle of AI emotion and sentiment analysis in computer vision?. For more information, please follow other related articles on the PHP Chinese website!