標(biāo)題: Titlebook: Discriminative Learning in Biometrics; David Zhang,Yong Xu,Wangmeng Zuo Book 2016 Springer Science+Business Media Singapore 2016 Biometric [打印本頁] 作者: Iodine 時間: 2025-3-21 18:02
書目名稱Discriminative Learning in Biometrics影響因子(影響力)
書目名稱Discriminative Learning in Biometrics影響因子(影響力)學(xué)科排名
書目名稱Discriminative Learning in Biometrics網(wǎng)絡(luò)公開度
書目名稱Discriminative Learning in Biometrics網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Discriminative Learning in Biometrics被引頻次
書目名稱Discriminative Learning in Biometrics被引頻次學(xué)科排名
書目名稱Discriminative Learning in Biometrics年度引用
書目名稱Discriminative Learning in Biometrics年度引用學(xué)科排名
書目名稱Discriminative Learning in Biometrics讀者反饋
書目名稱Discriminative Learning in Biometrics讀者反饋學(xué)科排名
作者: LAIR 時間: 2025-3-21 20:31 作者: 條約 時間: 2025-3-22 03:04
https://doi.org/10.1007/978-981-97-3629-4 to some discriminative learning tools that are commonly used in biometrics. A clear understanding of these techniques could be of essential importance in the sense that it forms the foundation for much of the subsequent parts in this book.作者: 上下連貫 時間: 2025-3-22 06:52
https://doi.org/10.1007/978-981-19-4859-6on, segmentation, classification, and visual tracking. In this chapter, we first summarize some frameworks of sparse representation, and then we give a brief introduction to the representation by dictionary learning algorithm. Based on the sparse representation, we present a novel multiple representations for image classification.作者: Estrogen 時間: 2025-3-22 09:43
Ecology and Sustainable Development in Japane a brief review of palmprint authentication methods in Sect.?.. Section?. describes the conventional coding-based palmprint identification methods. In Sects.?. and ., two improved coding-based palmprint authentication methods are presented.作者: 譏笑 時間: 2025-3-22 16:31
Xuan Lam Nguyen,Kaliappa Kalirajanhese possible changes, so it is hard to obtain very high accuracy for real-world face recognition. In this chapter, we present some effective schemes based on competent virtual face images to overcome the above problems. The adopted schemes and algorithms also seem to be applicable for some other applications.作者: 譏笑 時間: 2025-3-22 18:59
Karembe F. Ahimbisibwe,Tiina Kontinenlying rationales are also provided for the better understanding of these methods. In this chapter, we will give a further discussion about the book and present some remarks on the future development of discriminative learning for biometric recognition.作者: 沖突 時間: 2025-3-22 23:09 作者: 委屈 時間: 2025-3-23 01:35 作者: HEED 時間: 2025-3-23 06:53
Sparse Representation-Based Classification for Biometric Recognitionon, segmentation, classification, and visual tracking. In this chapter, we first summarize some frameworks of sparse representation, and then we give a brief introduction to the representation by dictionary learning algorithm. Based on the sparse representation, we present a novel multiple representations for image classification.作者: LARK 時間: 2025-3-23 12:46
Discriminative Features for Palmprint Authenticatione a brief review of palmprint authentication methods in Sect.?.. Section?. describes the conventional coding-based palmprint identification methods. In Sects.?. and ., two improved coding-based palmprint authentication methods are presented.作者: 獨(dú)輪車 時間: 2025-3-23 15:03
Discriminative Learning via Encouraging Virtual Face Imageshese possible changes, so it is hard to obtain very high accuracy for real-world face recognition. In this chapter, we present some effective schemes based on competent virtual face images to overcome the above problems. The adopted schemes and algorithms also seem to be applicable for some other applications.作者: STRIA 時間: 2025-3-23 22:05
Discussions and Future Worklying rationales are also provided for the better understanding of these methods. In this chapter, we will give a further discussion about the book and present some remarks on the future development of discriminative learning for biometric recognition.作者: FOR 時間: 2025-3-24 02:14
Book 2016ir applications in palmprint authentication, face recognition and multi-biometrics. The ideas, algorithms, experimental evaluation and underlying rationales are also provided for a better understanding of these methods. Lastly, it discusses several promising research directions in the field of discriminative biometric recognition..?.作者: 騎師 時間: 2025-3-24 06:14 作者: Nonconformist 時間: 2025-3-24 09:02
Anthony Alexander,Izabela Delabren effective palmprint identification method via the fusion of the left and right palmprints, which can be viewed as the fusion method of multiple traits with the same category. Then, we introduce another personal identification method via the fusion of the palmprint and palmvein, which uses multiple traits from the different category.作者: 流動才波動 時間: 2025-3-24 10:53
Ecology and Sustainable Development in Japanmatching algorithm for 2D palmprint verification, and then present a 3D features matching algorithm for 3D palmprint authentication. Some experiments are reported to illustrate the robustness of the presented algorithm in Sect.?.. The chapter is concluded in Sect.?..作者: 欄桿 時間: 2025-3-24 17:43 作者: Kaleidoscope 時間: 2025-3-24 21:42
Orientation Features and Distance Measure of Palmprint Authentication give a brief introduction to the orientation code-based methods in Sect.?.. In Sects.?. and ., two novel multiscale orientations code-based algorithms are presented. Section?. introduces an improved distance measure method for palmprint authentication.作者: climax 時間: 2025-3-25 01:01 作者: 蜈蚣 時間: 2025-3-25 05:44
Discriminative Learning in Biometricsirst give an overview on the systems in terms of the input features and common applications. After that, we will provide a self-contained introduction to some discriminative learning tools that are commonly used in biometrics. A clear understanding of these techniques could be of essential importanc作者: 表示向下 時間: 2025-3-25 11:26
Metric Learning with Biometric Applicationsesent two novel metric learning methods based on a support vector machine (SVM). We then present a kernel classification framework for metric learning that can be implemented efficiently by using the standard SVM solvers. Some novel kernel metric learning methods, such as the double-SVM and the trip作者: 拉開這車床 時間: 2025-3-25 13:44
Sparse Representation-Based Classification for Biometric Recognitionthod has received much attention in recent years and is widely applied in many fields, such as image denoising, debluring, restoration, super-resolution, segmentation, classification, and visual tracking. In this chapter, we first summarize some frameworks of sparse representation, and then we give 作者: 誘使 時間: 2025-3-25 16:13
Discriminative Features for Palmprint Authentications, which extract the coding features of palmprint images, are among the most promising palmprint authentication methods. In this chapter, we first give a brief review of palmprint authentication methods in Sect.?.. Section?. describes the conventional coding-based palmprint identification methods. I作者: Lipoprotein(A) 時間: 2025-3-25 23:59
Orientation Features and Distance Measure of Palmprint AuthenticationFor the orientation code-based methods, the orientation extraction and distance measure are two essential issues for palmprint verification. In this chapter, some efficient orientation extraction methods and a novel distance measure method are presented. The chapter is organized as follows. We first作者: 溺愛 時間: 2025-3-26 03:07 作者: Devastate 時間: 2025-3-26 04:41 作者: photopsia 時間: 2025-3-26 11:39 作者: 懸掛 時間: 2025-3-26 13:20 作者: 莊嚴(yán) 時間: 2025-3-26 17:07 作者: RODE 時間: 2025-3-27 00:00
Metric Learning with Biometric Applicationsesent two novel metric learning methods based on a support vector machine (SVM). We then present a kernel classification framework for metric learning that can be implemented efficiently by using the standard SVM solvers. Some novel kernel metric learning methods, such as the double-SVM and the triplet-SVM, are also introduced in this chapter.作者: homocysteine 時間: 2025-3-27 03:42 作者: 改正 時間: 2025-3-27 06:49
https://doi.org/10.1007/978-981-10-2056-8Biometrics; Discriminative learning; Palmprint authentication; Face recognition; Multi-biometrics; Patter作者: 遭遇 時間: 2025-3-27 11:54
978-981-10-9515-3Springer Science+Business Media Singapore 2016作者: 交響樂 時間: 2025-3-27 14:34
https://doi.org/10.1007/978-981-97-3629-4irst give an overview on the systems in terms of the input features and common applications. After that, we will provide a self-contained introduction to some discriminative learning tools that are commonly used in biometrics. A clear understanding of these techniques could be of essential importanc作者: 延期 時間: 2025-3-27 18:48
https://doi.org/10.1007/978-981-19-4859-6esent two novel metric learning methods based on a support vector machine (SVM). We then present a kernel classification framework for metric learning that can be implemented efficiently by using the standard SVM solvers. Some novel kernel metric learning methods, such as the double-SVM and the trip作者: 巧辦法 時間: 2025-3-27 23:40 作者: 前面 時間: 2025-3-28 03:20 作者: Eructation 時間: 2025-3-28 06:28 作者: 換話題 時間: 2025-3-28 11:21 作者: VOC 時間: 2025-3-28 15:23 作者: 膠水 時間: 2025-3-28 19:03
https://doi.org/10.1007/978-981-16-6734-3ognition. Sparse representation also has a good performance in both theoretical research and practical applications. Many different algorithms have been proposed for sparse representation. In this chapter, we will mainly introduce the application of the sparse representation in fields of face recogn作者: violate 時間: 2025-3-29 01:55 作者: Mets552 時間: 2025-3-29 06:49
Karembe F. Ahimbisibwe,Tiina Kontinenes with several representative methods of discriminative learning for biometric recognition. The ideas, algorithms, experimental evaluation, and underlying rationales are also provided for the better understanding of these methods. In this chapter, we will give a further discussion about the book an作者: 中世紀(jì) 時間: 2025-3-29 08:48
https://doi.org/10.1007/978-981-19-4859-6esent two novel metric learning methods based on a support vector machine (SVM). We then present a kernel classification framework for metric learning that can be implemented efficiently by using the standard SVM solvers. Some novel kernel metric learning methods, such as the double-SVM and the triplet-SVM, are also introduced in this chapter.作者: 健談的人 時間: 2025-3-29 11:45
https://doi.org/10.1007/978-981-16-6734-3ognition. Sparse representation also has a good performance in both theoretical research and practical applications. Many different algorithms have been proposed for sparse representation. In this chapter, we will mainly introduce the application of the sparse representation in fields of face recognition.作者: Ligament 時間: 2025-3-29 18:09
David Zhang,Yong Xu,Wangmeng ZuoSummarizes the latest studies on discriminative learning methods and their applications to biometric recognition.Covers different biometric recognition technologies, including face recognition, palmpr作者: Prophylaxis 時間: 2025-3-29 22:12
http://image.papertrans.cn/e/image/281228.jpg作者: MONY 時間: 2025-3-30 03:01
10樓作者: Panacea 時間: 2025-3-30 05:20
10樓
