Inspiration
Students often struggle to locate professors for tasks such as getting lab files checked, obtaining document approvals, or seeking advice. It can take hours or even days to find the right professor since they are often in class or unavailable in their offices. Class representatives rarely have accurate information, and responses from professors are frequently delayed. This inconvenience inspired the idea of creating an AI-based system that helps students easily find professors and know their availability in real time.
What it does
Faculty Finder is an AI-powered faculty availability system that predicts which professors are free by analyzing class timetables and uses face recognition to instantly identify a faculty member and display their schedule. The system allows students to know when and where a professor is available without needing to ask around or wait for responses. It ensures quick access to information, saving time and improving communication between students and faculty.
How we built it
The system was built using a combination of machine learning and computer vision technologies. A face recognition model was trained to identify faculty members based on their facial images. The system is connected to a database containing faculty details and class timetables. Using this data, an AI model predicts professors’ free slots and displays their schedules in real time. The interface, developed using HTML, CSS, JavaScript, and Python, enables students to either search for a professor or scan their face to retrieve availability information instantly.
Challenges we ran into
The main challenges included training the face recognition model with accurate faculty images, integrating the timetable data efficiently, and ensuring prediction accuracy. Maintaining privacy and data security while handling facial information and real-time schedule updates was also a significant concern.
What was made A face recognition system uptill now.
What we learned
Through this project, we learned how to combine artificial intelligence, computer vision, and data analysis to solve real-world issues. We also understood the importance of data accuracy, model training, and ethical handling of facial recognition technology.
What’s next for Faculty Finder
Future developments include building a mobile app version, integrating live location tracking for professors using campus Wi-Fi, improving face recognition accuracy with deep learning, and allowing professors to manually update their availability through a simple dashboard.
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