Inspirit AI Scholars Day 7

Today, we learned about how exactly AI detects emotions on a person's face. Basically, emotion recognition takes facial detection a step further by using AI algorithms to analyze facial expressions and interpret the emotional states of individuals. These algorithms are trained on vast datasets of labeled facial images, allowing the AI models to learn patterns and correlations between facial features and emotions.

Here are the exact steps and protocols AI takes to successfully detect emotion.

Step 1 - Facial Landmark Detection: The AI system identifies key facial landmarks, such as the positions of the eyes, eyebrows, mouth, and nose. These landmarks serve as reference points for further analysis.

Step 2 - Feature Extraction: The AI algorithm analyzes various facial features, such as eyebrow movement, lip curvature, and eye openness, to extract meaningful data related to emotions. It looks for subtle changes in these features to distinguish between different emotional states.

Step 3 - Machine Learning and Deep Learning: AI models employ machine learning and deep learning techniques to process the extracted features and build models that can recognize emotional patterns. By training on large datasets, these models can accurately predict emotions based on facial expressions.

Step 4 - Classification: After extracting the features and training the model, the AI system classifies facial expressions into different emotional states, such as happiness, sadness, anger, surprise, fear, and disgust. This classification allows for a more nuanced understanding of human emotions.

We, in our project will be writing a basic program which has all of these components and in the end, out project will be able to detect basic emotions! So excited to start the code in next week's meeting!