World's Best Scientists 2026 revealed!

Overview

Yongzhen Huang is affiliated with the Chinese Academy of Sciences in China. Their research spans the fields of Engineering, Physics and Astronomy, and Computer Science, with a particularly strong focus on Electrical and Electronic Engineering, Atomic and Molecular Physics and Optics, Computer Vision and Pattern Recognition, Biomedical Engineering, and Artificial Intelligence.

The main scientific topics that Huang investigates include Photonic and Optical Devices, Advanced Fiber Laser Technologies, Semiconductor Lasers and Optical Devices, Gait Recognition and Analysis, Nonlinear Dynamics and Pattern Formation, Advanced Fiber Optic Sensors, and Advanced Photonic Communication Systems.

Huang has published research in various reputable venues. Frequent publication outlets include:

  • Optics Express
  • arXiv (Cornell University)
  • Journal of Lightwave Technology
  • Optics Letters
  • Applied Optics

Some of Huang's recent papers are:

  • "Plasmonic Nanolasers in On-Chip Light Sources: Prospects and Challenges," 2020, ACS Nano
  • "Chaotic microlasers caused by internal mode interaction for random number generation," 2022, Light Science & Applications
  • "Development of a random forest model for hypotension prediction after anesthesia induction for cardiac surgery," 2021, World Journal of Clinical Cases
  • "Random bit generation based on a self-chaotic microlaser with enhanced chaotic bandwidth," 2023, Nanophotonics
  • "Generation of laser chaos with wide-band flat power spectrum in a circular-side hexagonal resonator microlaser with optical feedback," 2020, Optics Express

Frequent co-authors collaborating with Huang include:

  • Jin-Long Xiao
  • Yue-De Yang
  • You-Zeng Hao
  • Ji-Liang Wu
  • You-Ling Chen

Best Publications

  • A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs

    Zifeng Wu;Yongzhen Huang;Liang Wang;Xiaogang Wang

  • Deep semantic ranking based hashing for multi-label image retrieval

    Fang Zhao;Yongzhen Huang;Liang Wang;Tieniu Tan

  • Facial Expression Recognition Based on Deep Evolutional Spatial-Temporal Networks

    Kaihao Zhang;Yongzhen Huang;Yong Du;Liang Wang

  • Look and Think Twice: Capturing Top-Down Visual Attention with Feedback Convolutional Neural Networks

    Chunshui Cao;Xianming Liu;Yi Yang;Yinan Yu

  • A model-based gait recognition method with body pose and human prior knowledge

    Rijun Liao;Shiqi Yu;Shiqi Yu;Weizhi An;Yongzhen Huang

  • GaitPart: Temporal Part-Based Model for Gait Recognition

    Chao Fan;Yunjie Peng;Chunshui Cao;Xu Liu

  • Feature Coding in Image Classification: A Comprehensive Study

    Yongzhen Huang;Zifeng Wu;Liang Wang;Tieniu Tan

  • Auto-encoder Based Data Clustering

    Chunfeng Song;Feng Liu;Yongzhen Huang;Liang Wang

  • Invariant feature extraction for gait recognition using only one uniform model

    Shiqi Yu;Haifeng Chen;Qing Wang;Linlin Shen

  • Salient coding for image classification

    Yongzhen Huang;Kaiqi Huang;Yinan Yu;Tieniu Tan

  • GaitGANv2: Invariant gait feature extraction using generative adversarial networks

    Shiqi Yu;Rijun Liao;Weizhi An;Haifeng Chen

  • Pose-Based Temporal-Spatial Network (PTSN) for Gait Recognition with Carrying and Clothing Variations

    Rijun Liao;Chunshui Cao;Edel B. Garcia;Shiqi Yu

  • Gait Lateral Network: Learning Discriminative and Compact Representations for Gait Recognition.

    Saihui Hou;Chunshui Cao;Xu Liu;Yongzhen Huang

  • GaitNet: An end-to-end network for gait based human identification

    Chunfeng Song;Yongzhen Huang;Yan Huang;Ning Jia

  • Cross-View Gait Recognition by Discriminative Feature Learning

    Yuqi Zhang;Yongzhen Huang;Shiqi Yu;Liang Wang

  • UA-DETRAC 2017: Report of AVSS2017 & IWT4S Challenge on Advanced Traffic Monitoring

    Siwei Lyu;Ming-Ching Chang;Dawei Du;Longyin Wen

  • Kinship Verification with Deep Convolutional Neural Networks.

    Kaihao Zhang;Yongzhen Huang;Chunfeng Song;Hong Wu

  • A comprehensive study on gait biometrics using a joint CNN-based method

    Yuqi Zhang;Yongzhen Huang;Liang Wang;Shiqi Yu

  • Learning Representative Deep Features for Image Set Analysis

    Zifeng Wu;Yongzhen Huang;Liang Wang

  • Enhanced Biologically Inspired Model for Object Recognition

    Yongzhen Huang;Kaiqi Huang;Dacheng Tao;Tieniu Tan

Frequent Co-Authors

Liang Wang
Liang Wang Chinese Academy of Sciences
Tieniu Tan
Tieniu Tan Chinese Academy of Sciences
Kaiqi Huang
Kaiqi Huang Chinese Academy of Sciences
Dacheng Tao
Dacheng Tao Nanyang Technological University
Shu Wu
Shu Wu Chinese Academy of Sciences
Wankou Yang
Wankou Yang Southeast University
Xuelong Li
Xuelong Li China Telecom (China)
Linlin Shen
Linlin Shen Shenzhen University
Changyin Sun
Changyin Sun Southeast University
Longyin Wen
Longyin Wen ByteDance

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