In the era of internet shopping brick and mortar stalls still have an appeal for many shoppers. Many consumers still enjoy browsing through physical products and comparing in-store deals to find the best bargains. Food sellers and clothing stores are the primary focus of in-store shopping and in-both sectors companies are still looking to get an edge and improve the customer experience.
Facial recognition AI has the potential to make in-store shopping safer and more convenient. Computer vision based AI models have the ability to recognise individuals in digital images and video. This developing capacity is behind a number of exciting AI innovations that could soon be commonplace in stores around the world.
However, the success of this technology depends on support from data annotation specialists, like Keymakr.
Firstly, this blog will look at how image and video annotation help facial recognition models to learn. Secondly, we will identify the important use cases that are made possible by facial recognition. And finally, we will show how data annotation services can give AI companies in this sector a vital edge.
How AI Models learn to recognise faces
AI facial recognition models are trained using annotated image and video data. In practice this involves human annotators using tools to add information to digital images. In the case of facial annotation training data annotators primarily use a technique known as point annotation.
Point annotation is when annotators add small dots to key locations on images, such as eyes, noses and lips on an image of a human face. Machine learning algorithms are able to more easily locate these features, as a result, and learn how they are uniquely positioned on each person's face.
Applying this technique to video can be challenging. Annotators must locate facial features in every frame of video footage, this effectively means annotating thousands of individual images. However, video annotation is vital for a number of AI applications.
Applications powered by facial recognition
Facial recognition AI models can make the in-store shopping experience more pleasant for customers whilst saving retailers money. The following use cases integrate this technology in a number of interesting ways:
- Smart checkouts: Retailers can save money by installing self-checkouts in supermarkets. However, self-service solutions are often vulnerable to theft and fraud. Stores can improve loss prevention with the help of facial recognition models.
By recognising individuals that have previously shoplifted smart checkouts can support security staff and give adequate warning of potential theft scenarios.
- Sentiment analysis: Facial recognition can also mean understanding human emotions. If AI models can interpret the feelings of shoppers then they can also gain some understanding of which products appeal to consumers. Sentiment analysis could help stores tailor their product ranges for the true desires of today’s shoppers.
- Rewarding loyalty: Facial recognition models can also be used to reward loyal customers. AI powered cameras can recognise repeat shoppers and help craft loyalty programs that keep customers coming back.
- Improving the shopping experience: The end goal of facial recognition technology in stores is to create a shopping experience that fits each individual. Facial recognition enables AI systems to create personalized product recommendations based on shoppers’ purchasing habits.
The best solution for image and video annotation
The applications laid out above could make in store shopping better for everyone. As a result Keymakr is proud to support innovators in this sector with outstanding data annotation services. Keymakr’s in-house team of annotators is led by experienced managers. And our proprietary annotation tools ensure that image and video labeling is accurate and delivered on time.
Contact a team member to book your personalized demo today.