Machine Learning: Face Recognition

Authors

Mohammed E. Amin, CUNY New York City College of TechnologyFollow

Document Type

Poster

Publication Date

5-9-2024

Abstract

This project explores the cutting-edge intersection of machine learning (ML) and face recognition (FR) technology, utilizing the OpenCV library to pioneer innovative applications in real-time security and user interface enhancement. By processing live video feeds, our system encodes visual inputs and employs advanced face recognition algorithms to accurately identify individuals from a database of photos. This integration of machine learning with OpenCV not only showcases the potential for bolstering security systems but also enriches user experiences across various technological platforms. Through a meticulous examination of unique facial features and the application of sophisticated ML algorithms and neural networks, our project extends the utility of face recognition technology into realms such as healthcare, marketing, and social media by offering personalized services and enhancing public safety. Moreover, our development framework is adaptable for mobile environments, compatible with both iOS and Android platforms, and incorporates third-party services like Microsoft's Face API and Amazon Rekognition for broader application. The project underscores the significance of extensive datasets, like the Viola-Jones algorithm in OpenCV, to improve the precision and reliability of face recognition systems, thereby presenting a versatile solution for a myriad of applications from smart banking to diagnosing genetic disorders. This research not only reflects the technological advancements in machine learning and computer vision but also demonstrates the practical integration of these technologies in creating secure and user-centric applications.

Comments

This poster was presented at the 40th Semi-Annual Dr. Janet Liou-Mark Honors & Undergraduate Research Poster Presentation, May 9, 2024.

Mentor: Prof. Marcos Pinto (Computer Systems Technology).