Xingming Zhang
Papers
1
Total Citations
27
H-Index
1
About
Xingming Zhang is a leading researcher in computer vision and affective computing, with a primary focus on facial expression recognition and deep learning optimization. His most influential work, “Convolution by Multiplication: Accelerated Two-Stream Fourier Domain Convolutional Neural Network for Facial Expression Recognition” (2021, 27 citations), introduces a groundbreaking approach that accelerates convolutional neural networks by performing operations in the Fourier domain. This innovation significantly reduces computational complexity while maintaining high accuracy, addressing a critical bottleneck in real-time emotion analysis for applications in psychology, human-computer interaction, and robotics. Zhang’s contributions bridge the gap between theoretical efficiency and practical deployment, enabling faster, more robust facial expression recognition systems. His work has been widely cited in subsequent studies on lightweight neural architectures and frequency-domain learning, reflecting its impact on advancing both algorithm design and applied affective computing. By combining mathematical elegance with engineering pragmatism, Zhang continues to shape the future of intelligent human-machine interfaces.
Research Focus
Key Achievements
Top Papers
- 1