Abby Tan Chee Hong

About

Abby Tan Chee Hong is a researcher focused on advancing real-time computer vision systems, with a particular emphasis on robust face detection and tracking. Her work addresses a critical bottleneck in human-robot interaction (HRI) and video analysis: the trade-off between processing speed and accuracy. Tan’s key contribution is the development of the margin-based region of interest (MROI) technique, a hybrid approach that integrates Multi-Task Convolutional Neural Networks (MTCNN) with template matching. This method significantly improves detection robustness by intelligently narrowing the search space, thereby reducing computational load while maintaining high precision. Her most cited paper, “Hybrid Model with Margin-Based Real-Time Face Detection and Tracking” (2017), along with its 2018 follow-up, has garnered a combined 9 citations, establishing a foundational methodology for efficient, real-time facial recognition in dynamic environments. By directly tackling the problem of poor robustness in real-time setups, Tan’s work provides a practical solution for applications ranging from interactive robotics to surveillance. Her research stands as a valuable resource for engineers and scientists seeking to deploy reliable, low-latency vision systems in real-world scenarios.

Research Focus

Key Achievements

Top Papers

1. 1
   Hybrid Model with Margin-Based Real-Time Face Detection and Tracking

   5 citations · 2017
2. 2
   Using Margin-based Region of Interest Technique with Multi-Task Convolutional Neural Network and Template Matching for Robust Face Detection and Tracking System

   4 citations · 2018

Key Collaborators

Contact & Links