Due to the lack of large-scale, accurately annotated 3D facial datasets, most current 3D facial landmark detection algorithms rely on 2D texture assistance or non-real digital 3D faces. The performance of these algorithms is limited by the accuracy of texture mapping and the difference between digital faces and actual faces. To tackle these challenges, we built a large-scale, high-precision, and accurately annotated 3D facial database using a structured light system. Building upon this foundation, we proposed a novel point cloud sampling method and 3D facial landmark detection algorithm. This method utilizes a curvature-fused graph attention network to directly predict landmark coordinates from 3D point clouds. Firstly, we extracted a simplifying point set carrying curvature information from the original 3D facial point cloud via geometric point sampling. Then, we incorporated curvature-encoded positional information as the learning component of the attention module and employed it as a feature extractor to construct the network. We evaluated the performance of the method on BU-3DFE, FaceScape and our custom dataset. Compared to existing facial landmark detection algorithms, our model achieved higher accuracy. To facilitate future research on face related applications, we have made the database available on Github1.

Facial paralysis is a prevalent disorder affecting the facial nerve. In clinical settings, the severity of facial paralysis is typically assessed by physicians based on their subjective experience, evaluating the range of facial muscle movements and facial symmetry. To address these limitations, this paper proposes a method for the quantifiable evaluation of facial paralysis. We have developed a multi-view real-time facial acquisition system utilizing three infrared structured light units, enabling high-precision dynamic capture. Through non-rigid registration of the collected point cloud sequences, we generated a unified topological mesh sequence. Subsequently, we employed a novel facial asymmetry operator to quantitatively assess facial paralysis. Extensive experimental results demonstrate that the proposed method is both effective and accurate.

With the rapid advancement of deep learning, 2D facial landmark detection algorithms have achieved satisfying results. However, due to the absence of large-scale accurately annotated 3D facial datasets, most current 3D facial landmark detection algorithms rely on 2D texture assistance or non-real digital 3D faces. The performance of these algorithms is limited by the accuracy of 2D texture mapping onto 3D faces and the difference between digital faces and actual faces. To tackle these challenges, we have built a large-scale, high-precision 3D facial database using a structured light system. Facial landmarks within the database are marked multiple times to ensure accuracy. Building upon this foundation, we proposed a novel point cloud sampling method and 3D facial landmark detection algorithm. This method utilizes a curvature-fused graph attention network (CGAT) to directly predict landmark coordinates from 3D point clouds. Initially, we extracted a subsampled point set carrying curvature information from the original 3D facial point cloud via geometric point sampling (GPS). Then, we used curvature-encoded positional information as the learning component of the attention module and employed it as a feature extractor to construct the CGAT. We assessed the performance of CGAT on two datasets, BU-3DFE and CIE-H3DF (Ours). Compared to the existing facial landmark detection algorithms, CGAT achieves higher accuracy.

Head pose estimation (HPE) is a crucial task in pose recognition, but the existing HPE methods suffer from low robustness, low accuracy and inconvenience of contact measurement. In this paper, we build up an infrared structured light system for head pose estimation and propose an adaptive head pose estimation method to improve robustness and accuracy without the need for training. Firstly, we utilize a dynamic structured light system to acquire both the standard model and real-time dynamic data. Then, an iterative registration algorithm is proposed to adaptively segment the facial region which remains stable excluding distractions such as expressions and estimate the head pose. We experimentally evaluate the effectiveness of our method under conventional and large-angular head motion, and different expressions. The results demonstrate our method achieves highly accurate and robust real-time head pose estimation in a contactless manner.

In the (preprocessing) Decisional Diffie-Hellman (DDH) problem, we are given a cyclic group G with a generator g and a prime order N, and want to prepare some advice of S, such that we can efficiently distinguish (gx,gy,gxy) from (gx,gy,gz) in time T for uniformly and independently chosen x,y,z from [N]. This is a central cryptographic problem whose computational hardness underpins many widely deployed schemes such as the Diffie–Hellman key exchange protocol.

The medial axis transform (MAT) of an object is the set of all points inside the object that have more than one closest point on the object’s boundary. Representing sharp edges and corners of triangular meshes using MAT poses a complex challenge. While some researchers have proposed using zero-radius medial spheres to depict these features, they have not clearly articulated how to establish proper connections among them. In this paper, we propose a novel framework for computing MAT of a triangular mesh while preserving its features. The initial medial axis mesh obtained may contain erroneous edges, which are discussed and addressed in Section 3.3. Furthermore, during the simplification process, it is crucial to ensure that the medial spheres remain within the confines of the triangular mesh. Our algorithm excels in preserving critical features throughout the simplification procedure, consistently ensuring that the spheres remain enclosed within the triangular mesh. Experiments on various types of 3D models demonstrate the robustness, shape fidelity, and efficiency in representation achieved by our algorithm.

This paper presents a novel approach for the optimization of calibration parameters in structured light system (SLS). Different with conventional calibration algorithms, the proposed optimization algorithm is implemented in 3D space instead of 2D image space. The object used for parameter optimization can be a simple plane with some markers. A global optimal function is constructed to contain all the intrinsic and extrinsic parameters of the SLS. Using the primary calibration parameters by conventional methods as initial values, the optimal function can be solved by minimizing the 3D measurement errors like distance, angle between markers, and the planarity of the reference plane. Experimental results show that, 3D reconstruction accuracy can be greatly improved by the proposed approach in comparison with traditional SLS calibration methods.