Yi Wan

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About

Yi Wan is a researcher whose work centers on computer vision, machine learning, and human-computer interaction, with a particular focus on gesture recognition systems. His most notable contribution involves developing an innovative hand gesture recognition method that intelligently combines skin color detection with Support Vector Machine (SVM) classification. This approach addresses one of the persistent challenges in real-world computer vision applications — accurately isolating and identifying hand gestures under variable environmental conditions. By leveraging the YCbCr color space alongside Otsu's adaptive threshold algorithm for skin color segmentation, Wan's methodology demonstrated a practical and robust pipeline for gesture-based interaction systems. This work, which has garnered 5 citations since its 2019 publication, reflects growing interest in intuitive, vision-based control interfaces that could underpin applications ranging from assistive technologies to augmented reality systems. Wan's research contributes to the broader field of intelligent human-computer interaction, offering accessible solutions that balance computational efficiency with recognition accuracy — qualities increasingly valued as gesture-driven interfaces become more prevalent in everyday technological environments.