Emotion and image recognition work in AI. We are already fascinated by how image recognition works in identifying objects and places.
Now we will tackle more functions of image recognition.
Emotion And Image Recognition Work In AI
Well, as humans we are verbally declaring our emotions. However, they are another way to let others know how we feel.
But, maybe Artificial intelligence (AI) still on making a long way to perfections.
Here is some insight, Ekman’s classic theory of emotions is where the future machine learning is basing. The theory states that there are six human emotions.
And these six emotions are universal and can be applied to all cultures. However, many critics oppose the idea.
Also, they say that AI is capable of processing exaggerated operations. But not complex operations like emotions. Because their training set is not diverse enough.
However, despite this hindrance let see how emotion image recognition in AI.
Emotion And Image Recognition In AI
The ai determines your emotions based on various factors such as eyebrows, eyes, and lips. Also, the models can recognize eight emotions.
- neutral
- sad
- happy
- surprise
- fear
- anger
- disgust
- hatred
Furthermore, the R&D team at Sotrender breaks down on how emotion extractor works. And these two extractor system includes two machine learning models.
- emotion classifier model
- face detector
How Machine Learning Models Detect Faces and Emotion?
In faces ad emotions recognition it is called the state-of-art-machine learning algorithms. Also, it classified into two,
- Multi-Task Cascade Convolutional Neural Networks or MTCNN Architecture for detecting a face.
- ResNet50 Architecture for classifying emotions.
Here how it process:
- So, first of all, we need to do input an image. This image will be analyzed.
- The next step is to fill the input image to the face detector model.
- The faces in the image will be cropped out.
- And lastly the emotion classification step.
So how this can affect us, for example in our business?
- by emotion recognition, you can quickly show what emotions your brand’s communication. It can be in social media, images, or videos.
- Well, most of the ads engage in people. People showing satisfaction with the products or services. So this kind of analysis is outspoken.
- And lastly, the “faces of the brand” are presenting themselves. Like ambassador who will influence the consumers by their emotions.
Moreover, what’s more hype about this? You can always adjust your models base on how it will to social media data.
Challenges Of Emotion Recognition
Practically speaking, emotion recognition has a long way to go for perfections. Moreover, here are some situations that we can say that emotion recognition still struggle.
- People can read and label emotions in various ways. So the setting of the dataset result could be base on the one who sets.
- Moreover, the raising of eyebrows is a shock compared to happiness.
- Lastly, the issue of color. Models that are black men are usually detected as anger compared to white.
Our Emotion recognition might not perfect as of now and lots of flaws. But, there is always room for improvement.
