Reliable emotion detection is one of the holy grails of deep learning and AI research. The creation of AI models that are consistently able to recognise and contextualise human emotions promises revolutionary applications across a number of industries.
Widespread deployment of this technology will allow companies and other organisations to access analysis and insight into customers and individuals that will improve products and services. However, significant challenges remain. The ultimate success of emotion detection AI models is dependent, in part, on access to diverse, high-quality image datasets.
This blog will detail some of the exciting applications of emotion detection AI, as well as some of the key stumbling blocks. Professional annotation services, like Keymakr, are playing a part in overcoming these emerging challenges by ensuring that innovators are working with the best data for their projects.
The potential of emotion detection AI
Computer vision based AI applications that can recognize human emotions have varied array of potential use cases:
- Marketing: Understanding emotional states is essential for marketers looking to refine products. Emotion detection applications can be used to gauge responses to adverts or products in testing situations.
- Shopping: AI systems can gauge the sentiment of customers as they navigate around a shop. This can allow for better placement of displays and offers and overall contribute to a better shopping experience.
- Teaching: Applications are being created that will be able to analyse the emotions of students in real-time. Identifying boredom or excitement in a group of students can allow educators to adjust their lessons accordingly.
- Care: There is potential in the future for computer vision systems that can recognise adverse emotions in patients or those in care. This will enable healthcare workers to address needs as they emerge.
Whilst the potential of emotion detection technology is exciting, the progress of these systems is being slowed by a number of data based challenges:
- Cultural biases: There are a wide variety of ethnic and cultural differences in the way in which emotions are expressed. In India for example head shaking often indicates assent rather than “no”. This kind of differentiation can lead to significant challenges for AI models.
- Differences in appearance: The reliability of face detection can also be affected by diversity of appearance. Beards, hijabs, glasses, and masks could adversely impact the ability of a model to ascertain the correct emotion.
- Video data: In order for these systems to function usefully in the real world, they must be able to cope with input from video sources, such as CCTV. Annotating video footage can be a significant challenge for developers due to its time consuming nature.
Data annotation provides solutions
Keymakr is an image and video annotation provider that is helping to alleviate some of these pressing issues, by leveraging data collection expertise and a managed annotation workforce:
- Diverse data: Annotation services are often best suited to collect bespoke datasets that accurately reflect both the real world, and the specific needs of an emotion recognition project. Providers, like Keymakr, can construct datasets that correspond with particular cultural practices, or facial features, and annotate them accordingly.
- Point annotation: Accuracy in annotation is an essential component of successful AI development. Keymakr makes use of annotation techniques like key point annotation to ensure that relevant facial features are precisely located in every image.
- Outsourcing video annotation: Annotating video data is often a struggle for AI companies. Thousands of frames per small piece of footage means a significant annotation workload. Outsourcing to dedicated annotation teams is often a good solution. Keymakr’s experienced managers are able to ensure that labeling projects are completed on time and to the desired levels of quality.