Jinsu An

About

Jinsu An is a researcher at the forefront of efficient, real-time computer vision, with a primary focus on gender recognition and facial analysis. His most cited work, "Gender Recognizer Based on Human Face using CNN and Bottleneck Transformer Encoder" (2023), tackles a critical challenge: enabling accurate gender classification on low-cost, CPU-only devices for applications in Human-Robot Interaction and offline advertising. By ingeniously combining Convolutional Neural Networks with a Bottleneck Transformer Encoder, An’s architecture achieves a compelling balance between speed and precision—a vital contribution for deploying AI in resource-constrained, real-world environments. While his citation count is currently modest at 4, the practical significance of his work is underscored by its direct relevance to the growing demand for edge-AI solutions. An’s research stands out for its emphasis on operational efficiency without sacrificing accuracy, positioning him as an emerging voice in accessible, deployable facial analysis technology. His work promises to shape how machines perceive and interact with humans in everyday settings.

Research Focus

Key Achievements

Top Papers

1. 1
   Gender Recognizer Based on Human Face using CNN and Bottleneck Transformer Encoder

   4 citations · 2023

Key Collaborators

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