Inspiration
- Project Name: Red Queen
- Personalized ads/offers through AR: AR1 AR2
- Conversational Bot: Nola
- Face Recognition: NeoFace
What it does
Through Red Queen’s Vision, branches can identify, recognize, and start servicing their customer the moment they walk into the branch. With Foresight, branch managers will have insight into customer’s needs before they even need them and create personalized offerings. Through Recall, tellers can have information relevant to the customer displayed right as the customer approaches the counter. And finally, with Presence and Speech working with everything else, Emma, the Digital Human, can be right there with the customer whenever they need her.
How I built it
Technologies:
Face Detection & Recognition:
- Python
- Open CV (numpy, scikit, pillow, imutils)
Digital Human (Emma):
- Unity
- Fuse
- Mixamo
- Maya
Speech:
- Rasa Conversational AI
- Python Speech Recognition (pyttsx, PyAudio)
- Lyre Bird AI
- Salsa Lip Sync
Recall:
- FFDC
- Open API
Foresight:
- Azure ML Studio
What I learned
Most of the technologies used are new to the team
What's next for Red Queen
- Full Suite of ML Predictive models
- Deep Learning for Emma
- Red Queen: Omnipresence (Omni-Channel deployment of Emma on Mobile devices, Internet Banking, ATMs, Virtual Branches)
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