从单幅图像估算头部姿态的里奇曲率离散法

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Andrea Francesco Abate, Lucia Cascone, Michele Nappi
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引用次数: 0

摘要

头姿估计(HPE)在人机交互和生物识别框架增强等各种现实应用中至关重要。这项研究旨在利用网络曲率从单个图像中预测头部姿势。在网络中,某些节点组完成重要的功能角色。这项研究的重点是面部地标的相互作用,被认为是加权图中的顶点。实验表明,底层图的几何和拓扑结构能够检测不同头部姿态之间的相似性。研究了图的离散Ricci曲率的两个独立概念,即Ollivier-Ricci和Forman-Ricci曲率。这两种类型的Ricci曲率分别反映了网络的不同几何特性,作为回归模型的输入。来自BIWI、AFLW2000和Pointing ' 04数据集的结果表明,Ricci曲率的两种离散化密切相关,并且优于最先进的方法,包括基于地标和仅图像的方法。这证明了在各种应用中使用网络曲率的HPE的有效性和前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ricci curvature discretizations for head pose estimation from a single image
Head pose estimation (HPE) is crucial in various real-world applications, like human–computer interaction and biometric framework enhancement. This research aims to leverage network curvature to predict head pose from a single image. In networks, certain groups of nodes fulfill significant functional roles. This study focuses on the interactions of facial landmarks, considered as vertices in a weighted graph. The experiments demonstrate that the underlying graph geometry and topology enable the detection of similarities among various head poses. Two independent notions of discrete Ricci curvature for graphs, namely Ollivier–Ricci and Forman–Ricci curvatures, are investigated. These two types of Ricci curvature, each reflecting distinct geometric properties of the network, serve as inputs to the regression model. The results from the BIWI, AFLW2000, and Pointing‘04 datasets reveal that the two discretizations of Ricci’s curvature are closely related and outperform state-of-the-art methods, including both landmark-based and image-only approaches. This demonstrates the effectiveness and promise of using network curvature for HPE in diverse applications.
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来源期刊
Pattern Recognition
Pattern Recognition 工程技术-工程:电子与电气
CiteScore
14.40
自引率
16.20%
发文量
683
审稿时长
5.6 months
期刊介绍: The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.
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