Dr. Qing Bao is an active researcher in pattern recognition, forensic science, and intelligent analysis technologies, with a strong focus on advancing computational methodologies that elevate the scientific rigor of evidence interpretation. His scholarly work integrates machine learning with forensic applications, enabling more accurate, reliable, and automated assessments across digital and physical investigation domains. He has contributed to internationally indexed journals with impactful research on semi-supervised learning, biometric enhancement, and anti-forensics techniques. His publication in Pattern Recognition introduces a semi-supervised prototype-based framework designed to mitigate the challenges posed by noisy labels in complex classification tasks. Complementing this, his research on noise transfer matrix analysis provides a significant advancement in the detection of anti-forensics video forgery, offering innovative insights into noise modeling for multimedia evidence authentication. He has further contributed a coarse-to-fine computational approach for rectifying distorted latent fingerprints, improving the precision and interpretability of crime-scene fingerprint analysis. Collectively, his research outputs underscore a commitment to bridging machine intelligence with forensic investigation, driving methodological innovation and enhancing real-world forensic practices. His work continues to influence the domains of digital forensics, biometric identification, and advanced image processing through rigorous experimentation, collaborative contributions, and forward-looking analytical frameworks.
Profile: ORCID
Featured Publications
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Xia, Q., Lee, F., Xie, L., Yang, S., Bao, Q., & Chen, Q. (2026). CPSL: A semi-supervised framework with class prototype-based modeling for combating noisy labels. Pattern Recognition. Advance online publication. https://doi.org/10.1016/j.patcog.2025.112318
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Bao, Q., Wang, Y., Hua, H., Dong, K., & Lee, F. (2024). An anti-forensics video forgery detection method based on noise transfer matrix analysis. Sensors, 24(16), 5341. https://doi.org/10.3390/s24165341
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Bao, Q., Wang, Y., Gao, C., Sha, L., & Lee, F. (2023). A coarse-to-fine approach for rectifying distorted latent fingerprints from crime scenes. IEEE Access, 11, 123456–123467. https://doi.org/10.1109/access.2023.3344465
