This paper proposes a novel training remedy by decomposing the model into the weight parameters and the BN statistics in the training phase. Based on decomposing, we design a novel framework via meta-learning, called Decomposed Meta Batch Normalization (DMBN) for fast domain adaptation in face recognition.
This paper proposes an efficient method Face Alignment Policy Search (FAPS) to search the optimal alignment template in face recognition. A well-designed benchmark is also proposed to evaluate the searched policy.
We propose a novel regression framework, named 3DDFA_V2, to make a balance among speed, accuracy and stability.
Our model runs at over 50fps on a single CPU core (>200fps with ONNX acceleration) and outperforms other state-of-the-art heavy models simultaneously. Code and models are available at https://github.com/cleardusk/3DDFA_V2.
This paper constructs a new dataset Fine-Grained 3D face (FG3D) with 200k samples for training, and proposes a Fine-Grained reconstruction Network (FGNet) concentrating on shape modication by warping the network input and output to the UV space. FG3D is available at https://github.com/XiangyuZhu-open/Beyond3DMM.
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.
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.
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.
Reviewer for CVPR, ICCV, ECCV, AAAI, MM, IJCAI, IJCB, BMVC, FG, Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Transactions on Image Processing (TIP), Transactions on Cybernetics (T-C), IEEE Transactions on Neural Networks and Learning Systems (TNNLS), TBIOM, Knowledge-Based Systems, IEEE Access, Neurocomputing, IET Computer Vision, etc.