Artificial Intelligence (AI) innovators are designing computer vision models that can recognise and interpret human emotions. This ability has always been one of the core goals of AI developers and recent developments in machine learning are making emotion recognition a reality. Many AI applications stand to benefit from emotion recognition technology.
And many industries could be transformed with the help of AI. However, developers still need to overcome a number of challenges in order for this technology to be reliable. Data labeling tools, image, and video annotation tools, like Keylabs, can help AI companies to create powerful training datasets that for active learning.
Firstly, this blog will look at four emerging applications for emotion recognition in four different sectors. Secondly, we will look at some of the key challenges that still remain for emotion recognition and object detection AI. And finally, we will show how data annotation platform and computer vision annotation tools can help developers overcome these challenges.
Emotion recognition applications
The ability to recognise human emotions opens up a range of opportunities for AI in a number of industries. This technology can improve profits and lives in a number of ways:
- Retail insights: AI systems can gauge the sentiment of customers as they move around a shop. This helps retailers place displays and offers in better positions. As a consequence customers will have a better shopping experience.
- Caring AI: In the future computer vision systems may be used to recognise the emotions of patients and those in care. If patients are showing signs of distress, anger or other problematic emotions these computer vision models will be able to alert care workers. Care and working conditions for carers should improve as a result.
- Use cases for teaching: Applications are being created that will be able to analyze the emotions of students in real-time. Identifying boredom or excitement in a group of students can allow educators to adjust their lessons accordingly.
- Responsive marketing: Understanding emotional states is essential for marketers looking to refine products. Emotion detection applications can be used by project teams to gauge responses to adverts or products in testing situations.
Emotion recognition can be a powerful tool for many industries where quality data needs. However, there are a number of challenges that developers still need to overcome. Image and video annotation are an important part of machine learning models.
- Dealing with video: Video annotation can be costly and time-consuming. Each segment of AI pre-labeling training video contains thousands of individual frames. As a result, AI companies can struggle to label videos fast enough.
- Diverse appearances: Diversity of appearance can affect the performance of emotion recognition models. Beards, hijabs, glasses, and masks could make models less able to identify emotions correctly. This diversity must be accounted for in AI training data and machine learning algorithms.
- Cultural difference: There are a wide variety of ethnic and cultural differences in the way in which emotions are expressed. In India, for example, head shaking means yes and not no. These kinds of differences can cause significant challenges for emotion recognition models.
The developers and companies that work with emotion recognition need powerful data annotation tools with a friendly user interface to succeed. Keylabs offers AI innovators a number of key advantages:
- Project management: Keylabs allows managers to access detailed workforce analytics. This means that annotation tasks can be given to the highest performing workers. As a result, Keylabs makes annotations more precise.
- Task sharing: Keylabs links annotators and verifiers together. This ensures a smooth workflow and streamlined dataset labeling.
- Flexibility: Keylabs can incorporate your code. This allows for a fully customizable workflow.