Zekang Liu | Sign Language Recognition | Best Researcher Award

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Mr. Zekang Liu | Sign Language Recognition | Best Researcher Award

Eng.D Student at Tianjin University, China📖

Liu Zekang is a researcher and graduate student at Tianjin University, specializing in Electronic Engineering. With a background in Software Engineering, Liu has made significant contributions to the fields of artificial intelligence, Internet of Things (IoT), and vehicle detection technologies. He has been recognized for his academic achievements with awards such as the Third Prize at the National Forum on Innovation, Informatization, and Artificial Intelligence Development for Postdoctoral Researchers in 2021, and the Excellent Student Award from Tianjin University in 2023.

Profile

Scopus Profile

Education Background🎓

Liu Zekang’s educational journey began with a Bachelor’s degree in Software Engineering from Hebei University of Economics and Business, which he completed in 2017. He then pursued a Master’s degree in Software Engineering at Tianjin Normal University, graduating in 2019. Currently, Liu is a Ph.D. candidate in Electronic Engineering at Tianjin University, where he has been advancing his research since 2020. His academic background combines a solid foundation in software engineering with specialized expertise in electronic engineering, enabling him to explore and contribute to cutting-edge advancements in artificial intelligence, IoT, and real-time recognition systems.

Professional Experience🌱

Liu Zekang has actively engaged in research projects funded by the Tianjin Natural Science Foundation, where he contributed to the development of vehicle detection technology using smartphones and conducted significant studies in context-aware technologies within IoT environments. Additionally, he has worked on designing accessible communication systems leveraging key technologies. Liu’s research experience spans multiple aspects of intelligent transportation, sign language recognition, and deep learning applications.

Research Interests🔬

Liu’s research interests lie in the application of artificial intelligence to real-time recognition systems, including vehicle detection, sign language recognition, and IoT technologies. His focus is on leveraging convolutional neural networks (CNNs) and self-emphasizing networks to enhance real-time systems, with particular emphasis on applications that support accessibility, including continuous sign language recognition and background-independent computing.

Author Metrics

Liu has contributed to multiple high-impact publications in prestigious journals and conferences:

  1. Liu Zekang, Sun Huazhi, Ma Chunmei, et al. “Vehicle Recognition Model Based on Convolutional Neural Network with Multi-feature Fusion,” Computer Science, 2019.
  2. Hu L, Gao L, Liu Z, et al. “Self-emphasizing network for continuous sign language recognition,” Proceedings of the AAAI Conference on Artificial Intelligence, 2023.
  3. Hu L, Gao L, Liu Z, et al. “Continuous Sign Language Recognition with Correlation Network,” IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023.
  4. Liu Z, Feng W, Gao L, et al. “DBL-SC: background-independent sign language recognition based on spatial channel separation computation,” The Visual Computer, 2025.

Liu’s contributions to the field are positioned at the intersection of AI, accessibility technologies, and real-time processing, pushing the boundaries of what can be achieved through innovative computing.

Publications Top Notes 📄
  1. Scalable Frame Resolution for Efficient Continuous Sign Language Recognition
    Authors: Hu, L., Gao, L., Liu, Z., Feng, W.
    Journal: Pattern Recognition
    Year: 2024
    Volume: 145
    Article Number: 109903
    Citations: 5
    This article discusses scalable frame resolution techniques for enhancing the efficiency of continuous sign language recognition.
  2. AdaBrowse: Adaptive Video Browser for Efficient Continuous Sign Language Recognition
    Authors: Hu, L., Gao, L., Liu, Z., Pun, C.-M., Feng, W.
    Conference: Proceedings of the 31st ACM International Conference on Multimedia (MM 2023)
    Year: 2023
    Pages: 709–718
    Citations: 7
    This conference paper presents “AdaBrowse,” a system designed for efficient sign language recognition using an adaptive video browsing technique.
  3. Self-Emphasizing Network for Continuous Sign Language Recognition
    Authors: Hu, L., Gao, L., Liu, Z., Feng, W.
    Conference: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI 2023)
    Year: 2023
    Volume: 37
    Issue: 1
    Pages: 854–862
    Citations: 36
    This paper introduces a self-emphasizing network aimed at improving continuous sign language recognition through self-attention mechanisms.
  4. Temporal Lift Pooling for Continuous Sign Language Recognition
    Authors: Hu, L., Gao, L., Liu, Z., Feng, W.
    Conference: Lecture Notes in Computer Science (LNCS), 13695 LNCS
    Year: 2022
    Pages: 511–527
    Citations: 32
    This paper discusses “Temporal Lift Pooling,” a technique designed to enhance feature extraction for continuous sign language recognition.
  5. RNN-Transducer Based Chinese Sign Language Recognition
    Authors: Gao, L., Li, H., Liu, Z., Wan, L., Feng, W.
    Journal: Neurocomputing
    Year: 2021
    Volume: 434
    Pages: 45–54
    Citations: 44
    This article presents the use of RNN-Transducer models for the recognition of Chinese sign language, integrating both visual and acoustic data.

Conclusion

Liu Zekang is undoubtedly a highly deserving candidate for the Best Researcher Award. His innovative contributions to AI, particularly in continuous sign language recognition, his interdisciplinary expertise, and his commitment to addressing societal challenges through technology, set him apart as a leading researcher in his field. His ability to publish in high-impact venues, coupled with the recognition he has already received, highlights his potential for further success.

With a bit more emphasis on broadening his research scope and engaging more with industry, Liu could make even greater strides in creating real-world applications that benefit society. His research trajectory suggests that he will continue to make significant contributions, making him an excellent nominee for this prestigious award.