Jianzhu Guo（郭建珠）
I graduated from Southeast University with B.S. in GIS (Geographical Information System).
Now, I am a Ph.D. student at National Laboratory of Pattern Recognition, Chinese Academy of Sciences, supervised by Prof. Zhen Lei and Prof. Stan Z. Li, work closely with Associate Prof. Xiangyu Zhu, where I mainly research on face & vision, including face recognition, 3D face, face anti-spoofing, face analysis and meta learning, deep learning.
Email: jianzhu.guo@nlpr.ia.ac.cn or guojianzhu1994@foxmail.com GitHub / CV / CV_EN / Google Scholar / WeChat / LinkedIn / Blog Looking for a full-time job. |
|
News |
|
(
(
Publications |
|
Searching for Alignment in Face RecognitionXiaqing Xu, Qiang Meng, Yunxiao Qin, Jianzhu Guo, Chenxu Zhao, Feng Zhou, Zhen Lei AAAI 2021 paper / The alignment step in face recognition has attracted little attention. We firstly explore and highlight the effects of different alignment templates on face recognition. Then, for the first time, we try to search for the optimal template automatically. We construct a well-defined searching space by decomposing the template searching into the crop size and vertical shift, and propose an efficient method Face Alignment Policy Search (FAPS). Besides, a well-designed benchmark is proposed to evaluate the searched policy and experiments on our proposed benchmark validate the effectiveness of our method to improve face recognition performance. |
|
Towards Fast, Accurate and Stable 3D Dense Face AlignmentJianzhu Guo, Xiangyu Zhu, Yang Yang, Zhen Lei, Yang Fan, Stan Z. Li ECCV 2020 paper / supp / video1 / video2 / code / We propose a novel regression framework which makes a balance among speed, accuracy and stability. Firstly, on the basis of a lightweight backbone, we propose a meta-joint optimization strategy to dynamically regress a small set of 3DMM parameters, which greatly enhances speed and accuracy simultaneously. To further improve the stability on videos, we present a virtual synthesis method to transform one still image to a short-video incorporating in-plane and out-of-plane face moving. Our model runs at over 50fps on a single CPU core and outperforms other state-of-the-art heavy models simultaneously. Code and models will be available at https://github.com/cleardusk/3DDFA_V2. |
|
Beyond 3DMM Space: Towards Fine-grained 3D Face ReconstructionXiangyu Zhu, Fan Yang, Di Huang, Chang Yu, Hao Wang, Jianzhu Guo, Zhen Lei, Stan Z. Li ECCV 2020 paper / supp / code / We propose a solution to construct large-scale fi ne-grained 3D data from RGB-D images, and construct a new dataset Fine-Grained 3D face (FG3D) with 200k samples for training. We also propose a Fine-Grained reconstruction Network (FGNet) concentrating on shape modi cation by warping the network input and output to the UV space. The proposed method and code will be available at https://github.com/XiangyuZhu-open/Beyond3DMM. |
|
Learning Meta Face Recognition in Unseen DomainsJianzhu Guo, Xiangyu Zhu, Chenxu Zhao, Dong Cao, Zhen Lei, Stan Z. Li CVPR 2020 (Oral Presentation, acceptance rate 5%) arxiv / paper / code / We propose a novel face recognition method via meta-learning named Meta Face Recognition (MFR) to learn a generalized model performing well on unseen domains. Besides, we propose two benchmarks for generalized face recognition evaluation. The proposed benchmarks will be available at https://github.com/cleardusk/MFR. |
|
Domain Balancing: Face Recognition on Long-Tailed DomainsDong Cao, Xiangyu Zhu, Xingyu Huang, Jianzhu Guo, Zhen Lei CVPR 2020 arxiv / paper / This paper proposes a Domain Frequency Instructor (DFI), a light weighted Residual Balancing Mapping (RBM) block and a Domain Balancing Margin (DBM) to improve generalization on tailed domains. |
|
Improving Face Anti-Spoofing by 3D Virtual SynthesisJianzhu Guo, Xiangyu Zhu, Jinchuan Xiao, Zhen Lei, Genxun Wan, Stan Z. Li International Conference On Biometrics, ICB 2019 (Oral Presentation, acceptance rate 11.2%) arxiv / paper / We present a method to synthesize virtual spoof data in 3D space to improve face anti-spoofing. The synthetic virtual samples can significantly boost the anti-spoofing performance when combined with a proposed data balancing strategy. |
|
Face Synthesis for Eyeglass-Robust Face RecognitionJianzhu Guo, Xiangyu Zhu, Zhen Lei, Stan Z. Li CCF Chinese Conference on Biometric Recognition (CCBR2018), Urumqi, China, Aug. 11-12, 2018 arxiv / paper / code / In the application of face recognition, eyeglasses could significantly degrade the recognition accuracy. In this paper, we propose to address this problem in a virtual synthesis manner. |
|
Dominant and Complementary Emotion Recognition From Still Images of FacesJianzhu Guo, Zhen Lei, Jun Wan, et al. IEEE Access, 6(1), 26391-26403, 2018 paper / code / External journal version. |
|
|
Multi-modality Network with Visual and Geometrical Information for Micro Emotion RecognitionJianzhu Guo, Shuai Zhou, Jinlin Wu, Jun Wan, Xiangyu Zhu, Zhen Lei, Stan Z. Li FG2017, Washington, DC. May 30 –June 3, 2017 paper / code / Winner of face micro-emotion competition held on FG 2017. |
|
3DMA: A Multi-modality 3D Mask Face Anti-spoofing DatabaseJinchuan Xiao, Yinhang Tang, Jianzhu Guo, Yang Yang, Xiangyu Zhu, Zhen Lei, Stan Z. Li 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2019 paper / We release a multi-modality 3D mask face anti-spoofing database named 3DMA, which contains 920 videos of 67 genuine subjects wearing 48 kinds of 3D masks, captured in visual (VIS) and near-infrared (NIR) modalities. |
Under Review |
|
Decomposed Meta Batch Normalization for Fast Domain Adaptation in Face RecognitionJianzhu Guo, Xiangyu Zhu, Zhen Lei, Stan Z. Li Under Review (TIFS), 2020 |
Competitions |
|
Projects (Public) |
Service |
|
|
|
|
|
|
|