Andrea Francesco Abate, Lucia Cascone, Michele Nappi
{"title":"从单幅图像估算头部姿态的里奇曲率离散法","authors":"Andrea Francesco Abate, Lucia Cascone, Michele Nappi","doi":"10.1016/j.patcog.2025.111648","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"166 ","pages":"Article 111648"},"PeriodicalIF":7.5000,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Ricci curvature discretizations for head pose estimation from a single image\",\"authors\":\"Andrea Francesco Abate, Lucia Cascone, Michele Nappi\",\"doi\":\"10.1016/j.patcog.2025.111648\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":49713,\"journal\":{\"name\":\"Pattern Recognition\",\"volume\":\"166 \",\"pages\":\"Article 111648\"},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2025-04-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pattern Recognition\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0031320325003085\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pattern Recognition","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0031320325003085","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
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.
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.