AI Emotion Recognition
AI emotion recognition is a very active current field of computer vision research that involves facial emotion detection and the automatic assessment of sentiment from visual data. Human- machine interaction is an important area of research where artificially intelligent systems with visual perception aim to gain an understanding of human interaction.
What is Emotion AI?
Emotion AI, also called Affective Computing, is a rapidly growing branch of Artificial Intelligence that allows computers to analyze and understand human nonverbal signs such as facial expressions, body language, gestures, and voice tones to assess their emotional state. Hence, visual Emotion AI analyses face appearances in images and videos using computer vision technology to analyze an individual’s emotional status.
Visual AI Emotion Recognition
Emotion recognition is the task of machines trying to analyze, interpret and classify human emotion through the analysis of facial features. Among all the high-level vision tasks, Visual Emotion Analysis (VEA) is one of the most challenging tasks for the existing affective gap between low-level pixels and high-level emotions. Against all odds, visual emotion analysis is still promising as understanding human emotions is a crucial step towards strong artificial intelligence. With the rapid development of Convolutional Neural Networks (CNNs), deep learning became the new method of choice for emotion analysis tasks.
How AI-Emotion Analysis works
On a high level, an AI emotion application or vision system includes the following steps:
Step #1: Acquire the image frame from a camera feed (IP, CCTV, USB camera).
Step #2: Preprocessing of the image (cropping, resizing, rotating, color correction).
Step #3: Extract the important features with a CNN model
Step #4: Perform emotion classification
The basis of emotion recognition with AI is based on three sequential steps:
1. Face Detection in Images and Video Frames
2. Image Preprocessing
3. Emotion Classification AI Model
Applications of AI Emotion Recognition and Sentiment Analysis
There is a growing demand for AI emotion analysis in the AI and computer vision market. While it is not currently popular in large-scale use, solving visual emotion analysis tasks is expected to greatly impact real-world applications.
1. Opinion Mining
Opinion mining, or sentiment analysis, aims to extract people’s opinions, attitudes, and emotions from data. Conventional sentiment analysis concentrates primarily on textual content (for example, online user comments). However, visual sentiment analysis is beginning to receive attention since visual content such as images and videos became popular for self-expression in social networks. Opinion mining is an important strategy of smart advertising.
2. Medical Sentiment Analysis
Medical sentiment concerns the patient’s health status, medical conditions, and treatment. Its analysis and extraction have multiple applications in mental disease treatment, remote medical services, and human-computer interaction.
3.Emotional Relationship Recognition
Recent research developed an approach to recognize the emotional state of people to perform pairwise emotional relationship recognition. The challenge is to characterize the emotional relationship between two interacting characters using AI-based video analytics.
