Xiaoming Liu

Xiaoming Liu is a Chinese-American computer scientist and an academic. He is a professor in the Department of Computer Science and Engineering, MSU Foundation Professor as well as the Anil K. and Nandita Jain Endowed Professor of Engineering at Michigan State University.

Liu is most known for his works in the fields of computer vision, machine learning, and biometrics, with a particular focus on facial analysis and three-dimensional (3D) vision. Moreover, he is the recipient of the 2018 and 2023 Withrow Distinguished Scholar Award from the Michigan State University College of Engineering.

Liu is a fellow of the International Association for Pattern Recognition (IAPR) and The Institute of Electrical and Electronics Engineers (IEEE). Additionally, he is the associate editor of the journal IEEE Transactions on Pattern Analysis and Machine Intelligence.

Education Liu completed his Bachelor of Arts degree in Computer Science and Engineering from Beijing Information Technology Institute in 1997. In 2000, he obtained a Master of Science degree in Computer Science and Engineering from Zhejiang University under the supervision of Yueting Zhuang. This was followed by a Ph.D. in Electrical and Computer Engineering, supervised by Tsuhan Chen and Vijayakumar Bhagavatula from Carnegie Mellon University in 2004. and introduced an adaptable method called "CAFace" for understanding the connections between identity details across video frames and executing sequential recognition while streaming video.

One of the primary research objectives of Liu's recognition research is to cultivate trust between AI systems and their users. He led research in face presentation attack detection (PAD) and published papers. He created algorithms to mitigate bias in facial recognition by revealing that different demographic groups require distinct convolutional neural network (CNN) kernels, leading to an adaptive architecture that enhances accuracy while reducing bias. Moreover, he proposed a technique Model Parsing for reverse engineering GMs to understand their hyperparameters. His group also developed passive and proactive approaches to deep fake detection and localization.

Modeling Liu's modeling research has centered on image alignment and intrinsic image decomposition. He developed Boosted Appearance Model, a discriminative model for image alignment that uses a boosting-based classifier to distinguish between correctly aligned images with ground-truth landmarks and incorrectly aligned images with perturbed landmarks. Additionally, to address the limitations of BAM, he introduced BRM, which focused on learning a score function that is concave in the vicinity of the correct alignment, potentially improving alignment accuracy. Furthermore, he identified the challenge of aligning profile-view faces accurately and addressed this by developing approaches that treat image alignment as a 3D Morphable Models (3DMM) fitting problem, enabling the estimation of 3D facial landmarks.

3D perception

Awards and honors 2018 – Withrow Distinguished Scholar–Junior Award 2020 – Fellow, International Association for Pattern Recognition

Selected articles Zhu, X., Lei, Z., Liu, X., Shi, H., & Li, S. Z. (2016). Face alignment across large poses: A 3D solution. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 146–155). Tai, Y., Yang, J., & Liu, X. (2017). Image super-resolution via deep recursive residual network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3147–3155). Tai, Y., Yang, J., Liu, X., & Xu, C. (2017). MemNnet: A persistent memory network for image restoration. In Proceedings of the IEEE international conference on computer vision (pp. 4539–4547). Tran, L., Yin, X., & Liu, X. (2017). Disentangled representation learning GAN for pose-invariant face recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1415–1424) Liu, Y., Jourabloo, A., & Liu, X. (2018). Learning deep models for face anti-spoofing: Binary or auxiliary supervision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 389–398).