Hae-Min Moon

Papers

Total Citations

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About

Hae-Min Moon is a researcher focused on advancing face recognition technologies, particularly for Internet of Things (IoT) applications. Her key research areas include long-distance face recognition, linear discriminant analysis (LDA)-based algorithms, and user-friendly biometric interfaces. Moon's most cited work, "LDA-based face recognition using multiple distance training face images with low user cooperation" (2015, 14 citations), addresses a critical usability challenge: reducing user inconvenience during initial registration by enabling effective recognition from images captured at varying distances without requiring active user movement. Her follow-up study, "Long distance face recognition for enhanced performance of internet of things service interface" (2014, 5 citations), extends this approach to IoT environments, proposing an LDA-based algorithm that improves intelligent interface performance for networked objects. These contributions demonstrate Moon's commitment to making face recognition more practical and accessible for real-world applications, particularly in smart environments where seamless, low-effort user interaction is essential. Her work bridges the gap between algorithmic performance and user experience, offering solutions that enhance both security and convenience in biometric systems.