Local features contain crucial clues for face anti-spoofing. Convolutional neural networks (CNNs) are powerful in extracting local features, but the intrinsic inductive bias of CNNs limits the ability to capture long-range dependencies. This paper aims to develop a simple yet effective framework that is versatile in extracting both local information and long-range dependencies for face anti-spoofing. To this end, we propose a novel architecture, namely Conv-MLP, which incorporates local patch convolution with global multi-layer perceptrons (MLP). Conv-MLP breaks the inductive bias limitation of traditional full CNNs and can be expected to better exploit long-range dependencies. Furthermore, we design a new loss specifically for the face anti-spoofing task, namely moat loss. The moat loss benefits discriminative representations learning and can improve the generalization capability on unseen presentation attacks. In this work, multi-modal data are directly fused at the signal level to extract complementary features. Extensive experiments on single and multi-modal datasets demonstrate that Conv-MLP outperforms existing state-of-the-art methods while being more computationally efficient. The code is available at https://github.com/WeihangWANG/Conv-MLP .

In practical applications, the generalization capability of face anti-spoofing (FAS) models on unseen domains is of paramount importance to adapt to diverse camera sensors, device drift, environmental variation, and unpredictable attack types. Recently, various domain generalization (DG) methods have been developed to improve the generalization capability of FAS models via training on multiple source domains. These DG methods commonly require collecting sufficient real-world attack samples of different attack types for each source domain. This work aims to learn a FAS model without using any real-world attack sample in any source domain but can generalize well to the unseen domain, which can significantly reduce the learning cost. Toward this goal, we draw inspiration from the theoretical error bound of domain generalization to use negative data augmentation instead of real-world attack samples for training. We show that using only a few types of simple synthesized negative samples, e.g., color jitter and color mask, the learned model can achieve competitive performance over state-of-the-art DG methods trained using real-world attack samples. Moreover, a dynamic global common loss and a local contrast loss are proposed to prompt the model to learn a compact and common feature representation for real face samples from different source domains, which can further improve the generalization capability. Experimental results of extensive cross-dataset testing demonstrate that our method can even outperform state-of-the-art DG methods using real-world attack samples for training. The code for reproducing the results of our method is available at https://github.com/WeihangWANG/NDA-FAS.

State-of-the-art human detection methods focus on deep network architectures to achieve higher recognition performance, at the expense of huge computation. However, computational efficiency and real-time performance are also important evaluation indicators. This paper presents a fast real-time human detection and flow estimation method using depth images captured by a top-view TOF camera. The proposed algorithm mainly consists of head detection based on local pooling and searching, classification refinement based on human morphological features, and tracking assignment filter based on dynamic multi-dimensional feature. A depth image dataset record with more than 10k entries and departure events with detailed human location annotations is established. Taking full advantage of the distance information implied in the depth image, we achieve high-accuracy human detection and people counting with accuracy of 97.73% and significantly reduce the running time. Experiments demonstrate that our algorithm can run at 23.10 ms per frame on a CPU platform. In addition, the proposed robust approach is effective in complex situations such as fast walking, occlusion, crowded scenes, etc.

Phase unwrapping is a fundamental problem in Time-of-Flight (ToF) imaging, especially when high modulation frequency is used to achieve precise measurement accuracy. This paper proposes a novel double-frequency based phase unwrapping method for ToF sensors. The new method incorporates the idea of image segmentation to solve phase unwrapping region-by-region instead of pixel-by-pixel. The depth image is segmented based on the constraint between depth measurements and wrap numbers at two modulation frequencies. To avoid misclassification of the pixels around phase jump edges in the presence of inevitable noise, we employ a modified distance function to classify the edge pixels into the corresponding connected area. Furthermore, a graph model is used to accurately model the phase jumping relation between different connected areas. On this basis, we propose an MRF framework to fuse the amplitude and depth information to unwrap the phase. Experimental results on both synthetic and real-world data demonstrate that, the proposed method is robust to noise and outperforms state-of-the-art methods, while being highly efficient enabling real-time running.