IdentifiEYE has the potential to significantly improve the quality of life for individuals with face blindness by providing them with a reliable tool to identify people in real-time. My vision for the future is to expand the capabilities of IdentifiEYE to include additional features such as facial expression recognition and integration with smart devices for broader accessibility.
I built 'IdentifiEYE' application from the ground up, developing Python-based facial-recognition models and an AR interface in C#/.NET for real-time identification. I designed the technical architecture to integrate state-of-the-art AI algorithms with a seamless AR interface. One of the major challenges was ensuring the AI model could recognize faces under various lighting conditions and angles. This was overcome by training the model on a diverse dataset that included different lighting scenarios and face orientations.
Developed custom facial recognition models using Python, TensorFlow, and OpenCV. Trained on diverse datasets to ensure accuracy across different lighting conditions and angles.
Built the AR interface using C#/.NET and Unity, creating an intuitive heads-up display that provides real-time identification without disrupting the user's field of view.
Conducted extensive user testing with individuals experiencing face blindness to refine the interface and improve recognition accuracy based on real-world feedback
IdentifiEYE was inspired by the lack of effective tools for people with face blindness, a gap that pushed me to create a user-centered solution. Working directly with affected individuals at nursing homes provided insights that shaped the interface and improved recognition accuracy. This project deepened my belief in technology’s potential for social impact and taught me the value of empathy, feedback, and continuous iteration. If I could revisit it, I’d focus more on user training to support long-term adoption.
Presented at the International Business Research and Innovation Conference
Won Bronze Medal
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