We were inspired to enhance the customer experience and automate user feedback by the growing focus on customer service and the lack of consumer understanding.
What it does
Eigenfaces helps to enrich the customer and user experience by helping businesses understand their consumer patterns and providing optimized business strategies. We use facial recognition and emotion detection to collect real-time analysis of consumer behaviour and customer segmentation. In particular, Eigenfaces generates immediate reports that summarize the gender, age group, and emotional information of customers based on either a picture or video of their facial expression, which can often be easily obtained from existing surveillance cameras in the business.
How we built it
We used Python, IBM Watson API, Microsoft API, HTML, CSS, Bootstrap, and Flask.
Challenges we ran into
We had a bit of difficulty establishing a proper connection between the front and back end.
Accomplishments that we're proud of
We are very proud of being able to efficiently and effectively collaborate as a team to produce a functional product that delivers all of the basic elements within a limited time.
What we learned
Throughout the hackathon, we learned to organize and manage our time as well as expectations more reasonably.
What's next for Eigenfaces: Enhance and Enrich User Experience
The current version of our product only performs a basic data summary and analysis on a few different aspects of the customers and users through any uploaded audio and visual recordings. We plan on further developing the product to be able to provide more elaborate and insightful reports that can examine more aspects of the users through both uploaded and live recordings.
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