{"title":"对应的关键点约束稀疏表示三维人耳识别，每个人一个样本","authors":"Qinping Zhu, Zhichun Mu, Li Yuan","doi":"10.1049/bme2.12067","DOIUrl":null,"url":null,"abstract":"<p>When only one sample per person (OSPP) is registered in the gallery, it is difficult for ear recognition methods to sufficiently and effectively reduce the search range of the matching features, thus resulting in low computational efficiency and mismatch problems. A 3D ear biometric system using OSPP is proposed to solve this problem. By categorising ear images by shape and establishing the corresponding relationship between keypoints from ear images and regions (regional cluster) on the directional proposals that can be arranged to roughly face the ear image, the corresponding keypoints are obtained. Then, ear recognition is performed by combining corresponding keypoints and a multi-keypoint descriptor sparse representation classification method. The experimental results conducted on the University of Notre Dame Collection J2 dataset yielded a rank-1 recognition rate of 98.84%; furthermore, the time for one identification operation shared by each gallery subject was 0.047 ms.</p>","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"11 3","pages":"225-248"},"PeriodicalIF":1.8000,"publicationDate":"2022-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12067","citationCount":"2","resultStr":"{\"title\":\"Corresponding keypoint constrained sparse representation three-dimensional ear recognition via one sample per person\",\"authors\":\"Qinping Zhu, Zhichun Mu, Li Yuan\",\"doi\":\"10.1049/bme2.12067\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>When only one sample per person (OSPP) is registered in the gallery, it is difficult for ear recognition methods to sufficiently and effectively reduce the search range of the matching features, thus resulting in low computational efficiency and mismatch problems. A 3D ear biometric system using OSPP is proposed to solve this problem. By categorising ear images by shape and establishing the corresponding relationship between keypoints from ear images and regions (regional cluster) on the directional proposals that can be arranged to roughly face the ear image, the corresponding keypoints are obtained. Then, ear recognition is performed by combining corresponding keypoints and a multi-keypoint descriptor sparse representation classification method. The experimental results conducted on the University of Notre Dame Collection J2 dataset yielded a rank-1 recognition rate of 98.84%; furthermore, the time for one identification operation shared by each gallery subject was 0.047 ms.</p>\",\"PeriodicalId\":48821,\"journal\":{\"name\":\"IET Biometrics\",\"volume\":\"11 3\",\"pages\":\"225-248\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2022-03-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12067\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Biometrics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/bme2.12067\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Biometrics","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/bme2.12067","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 2
摘要
当图库中每个人只有一个样本(OSPP)注册时，耳识别方法难以充分有效地缩小匹配特征的搜索范围，从而导致计算效率低和不匹配问题。为了解决这一问题，提出了一种基于OSPP的三维耳生物识别系统。通过对耳图像进行形状分类，在大致面向耳图像的可排列方向建议上，建立耳图像关键点与区域(区域聚类)的对应关系，得到相应的关键点。然后，将相应的关键点与多关键点描述符稀疏表示分类方法相结合进行人耳识别。在University of Notre Dame Collection J2数据集上进行的实验结果显示，rank-1识别率为98.84%;此外，每个画廊受试者共享一次识别操作的时间为0.047 ms。
Corresponding keypoint constrained sparse representation three-dimensional ear recognition via one sample per person
When only one sample per person (OSPP) is registered in the gallery, it is difficult for ear recognition methods to sufficiently and effectively reduce the search range of the matching features, thus resulting in low computational efficiency and mismatch problems. A 3D ear biometric system using OSPP is proposed to solve this problem. By categorising ear images by shape and establishing the corresponding relationship between keypoints from ear images and regions (regional cluster) on the directional proposals that can be arranged to roughly face the ear image, the corresponding keypoints are obtained. Then, ear recognition is performed by combining corresponding keypoints and a multi-keypoint descriptor sparse representation classification method. The experimental results conducted on the University of Notre Dame Collection J2 dataset yielded a rank-1 recognition rate of 98.84%; furthermore, the time for one identification operation shared by each gallery subject was 0.047 ms.
IET BiometricsCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
5.90
自引率
0.00%
发文量
46
审稿时长
33 weeks
期刊介绍：
The field of biometric recognition - automated recognition of individuals based on their behavioural and biological characteristics - has now reached a level of maturity where viable practical applications are both possible and increasingly available. The biometrics field is characterised especially by its interdisciplinarity since, while focused primarily around a strong technological base, effective system design and implementation often requires a broad range of skills encompassing, for example, human factors, data security and database technologies, psychological and physiological awareness, and so on. Also, the technology focus itself embraces diversity, since the engineering of effective biometric systems requires integration of image analysis, pattern recognition, sensor technology, database engineering, security design and many other strands of understanding.
The scope of the journal is intentionally relatively wide. While focusing on core technological issues, it is recognised that these may be inherently diverse and in many cases may cross traditional disciplinary boundaries. The scope of the journal will therefore include any topics where it can be shown that a paper can increase our understanding of biometric systems, signal future developments and applications for biometrics, or promote greater practical uptake for relevant technologies:
Development and enhancement of individual biometric modalities including the established and traditional modalities (e.g. face, fingerprint, iris, signature and handwriting recognition) and also newer or emerging modalities (gait, ear-shape, neurological patterns, etc.)
Multibiometrics, theoretical and practical issues, implementation of practical systems, multiclassifier and multimodal approaches
Soft biometrics and information fusion for identification, verification and trait prediction
Human factors and the human-computer interface issues for biometric systems, exception handling strategies
Template construction and template management, ageing factors and their impact on biometric systems
Usability and user-oriented design, psychological and physiological principles and system integration
Sensors and sensor technologies for biometric processing
Database technologies to support biometric systems
Implementation of biometric systems, security engineering implications, smartcard and associated technologies in implementation, implementation platforms, system design and performance evaluation
Trust and privacy issues, security of biometric systems and supporting technological solutions, biometric template protection
Biometric cryptosystems, security and biometrics-linked encryption
Links with forensic processing and cross-disciplinary commonalities
Core underpinning technologies (e.g. image analysis, pattern recognition, computer vision, signal processing, etc.), where the specific relevance to biometric processing can be demonstrated
Applications and application-led considerations
Position papers on technology or on the industrial context of biometric system development
Adoption and promotion of standards in biometrics, improving technology acceptance, deployment and interoperability, avoiding cross-cultural and cross-sector restrictions
Relevant ethical and social issues
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
