A Complementary Fusion Strategy for RGB-D Face Recognition

Zheng, Haoyuan; Wang, Weihang; Wen, Fei; Liu, Peilin

doi:10.1007/978-3-030-98358-1_27

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…Despite the wide availability of large RGB face image datasets [3,27,32], similarly sized datasets containing RGB-D face images are not available yet. RGB-D face datasets contain a limited number of samples [8,31,57], or they have been captured without considering HMD occlusions [60] and any additional modalities. Hence, researchers mostly tend to use a synthetic dataset with a high degree of variety in order to solve their research problems related to human faces.…”

Section: Rgb-d Datasetmentioning

confidence: 99%

HEADSET: Human Emotion Awareness under Partial Occlusions Multimodal DataSET

Lohesara,

Freitas,

Guillemot

et al. 2023

IEEE Trans. Visual. Comput. Graphics

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Fig. 1: Details of our proposed dataset (HEADSET) in terms of modalities and use cases. (A): Textured 3D meshes with 6 facial expressions, (B): three types of 3D point cloud with participant wearing an HMD, (C): RGB images and depth maps from 3 views, (D): multi-view representation of LF, (E): point clouds evaluation results, (F): result of HMD removal, and (G): classified LF images with 6 facial expressions.

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Section: Rgb-d Datasetmentioning

confidence: 99%

HEADSET: Human Emotion Awareness under Partial Occlusions Multimodal DataSET

Lohesara,

Freitas,

Guillemot

et al. 2023

IEEE Trans. Visual. Comput. Graphics

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“…These files can vary in size and density of points and these depend mostly on the camera that is being used to generate such files, thousands of points can be found in a scene captured in a single shot, and, generally, the complexity of the processing of this information is increased proportionally with the quality of the camera and of the information it produces, the greater the detail, the greater point density. Novel methods can be found that use this type of information to solve problems in different fields, for example, in 3D reconstruction [6,7], simultaneous localization and mapping (SLAM) as in [8,9], navigation [10], object detection [11], mapping urban buildings [12], recovering building geometries [13,14], indoor scene reconstruction [7,15], computer vision as face recognition [16], segmentation with background removal [17], recognition tasks in robotics using scene modeling [18], navigation in agriculture [19], pedestrian detection [11], augmented reality (AR) [20], computerassisted surgery [21], 3D navigation for pedestrians and robots [22], ADAS (advanced driving assistance systems) [23], uncrewed aerial vehicles (UAVs) navigation [24], autonomous driving [25], body tracking [26], and RGB-D Multi-Camera Pose Estimation for 3D Reconstruction [27]. There are different sets of data or databases that are compiled and organized to facilitate the research paper using information from different scenarios represented in 3D point clouds [28].…”

Section: Introductionmentioning

confidence: 99%

A Robust Sphere Detection in a Realsense Point Cloud by USING Z-Score and RANSAC

et al. 2023

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Three-dimensional vision cameras, such as RGB-D, use 3D point cloud to represent scenes. File formats as XYZ and PLY are commonly used to store 3D point information as raw data, this information does not contain further details, such as metadata or segmentation, for the different objects in the scene. Moreover, objects in the scene can be recognized in a posterior process and can be used for other purposes, such as camera calibration or scene segmentation. We are proposing a method to recognize a basketball in the scene using its known dimensions to fit a sphere formula. In the proposed cost function we search for three different points in the scene using RANSAC (Random Sample Consensus). Furthermore, taking into account the fixed basketball size, our method differentiates the sphere geometry from other objects in the scene, making our method robust in complex scenes. In a posterior step, the sphere center is fitted using z-score values eliminating outliers from the sphere. Results show our methodology converges in finding the basketball in the scene and the center precision improves using z-score, the proposed method obtains a significant improvement by reducing outliers in scenes with noise from 1.75 to 8.3 times when using RANSAC alone. Experiments show our method has advantages when comparing with novel deep learning method.

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