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標題: Titlebook: Deep Learning for Biometrics; Bir Bhanu,Ajay Kumar Book 2017 Springer International Publishing AG, part of Springer Nature 2017 Deep Learn [打印本頁]

作者: 離開浮于空中    時間: 2025-3-21 17:49
書目名稱Deep Learning for Biometrics影響因子(影響力)




書目名稱Deep Learning for Biometrics影響因子(影響力)學科排名




書目名稱Deep Learning for Biometrics網絡公開度




書目名稱Deep Learning for Biometrics網絡公開度學科排名




書目名稱Deep Learning for Biometrics被引頻次




書目名稱Deep Learning for Biometrics被引頻次學科排名




書目名稱Deep Learning for Biometrics年度引用




書目名稱Deep Learning for Biometrics年度引用學科排名




書目名稱Deep Learning for Biometrics讀者反饋




書目名稱Deep Learning for Biometrics讀者反饋學科排名





作者: AMPLE    時間: 2025-3-22 00:18

作者: 辭職    時間: 2025-3-22 03:53
CMS-RCNN: Contextual Multi-Scale Region-Based CNN for Unconstrained Face Detectionacial periocular recognition, facial landmarking and pose estimation, facial expression recognition, 3D facial model construction, etc. Although the face detection problem has been intensely studied for decades with various commercial applications, it still meets problems in some real-world scenario
作者: 道學氣    時間: 2025-3-22 06:15
Latent Fingerprint Image Segmentation Using Deep Neural NetworkRBMs), and uses it to perform segmentation of latent fingerprint images. Artificial neural networks (ANN) are biologically inspired architectures that produce hierarchies of maps through learned weights or filters. Latent fingerprints are fingerprint impressions unintentionally left on surfaces at a
作者: TEN    時間: 2025-3-22 10:40
Finger Vein Identification Using Convolutional Neural Network and Supervised Discrete Hashingnal privacy and anonymity in during the identification process. The Convolutional Neural Network (CNN) has shown remarkable capability for learning biometric features that can offer robust and accurate matching. We introduce a new approach for the finger vein authentication using the CNN and supervi
作者: IRATE    時間: 2025-3-22 13:09

作者: IRATE    時間: 2025-3-22 17:21

作者: 纖細    時間: 2025-3-23 00:23

作者: genuine    時間: 2025-3-23 01:45

作者: 幾何學家    時間: 2025-3-23 09:14

作者: 釋放    時間: 2025-3-23 10:01
Learning Representations for Cryptographic Hash Based Face Template Protectioning ability of neural networks has enabled them to achieve state-of-the-art results in several fields, including face recognition. Consequently, biometric authentication using facial images has also benefited from this, with deep convolutional neural networks pushing the matching performance numbers
作者: Mobile    時間: 2025-3-23 17:20
Deep Triplet Embedding Representations for Liveness Detectionr an attacker it is relatively easy to build a fake replica of a legitimate finger and apply it directly to the sensor, thereby fooling the system by declaring its corresponding identity. In order to ensure that the declared identity is genuine and it corresponds to the individual present at the tim
作者: 著名    時間: 2025-3-23 20:57

作者: Mystic    時間: 2025-3-23 23:34

作者: 美麗的寫    時間: 2025-3-24 02:35

作者: NOT    時間: 2025-3-24 06:40
Gender Classification from NIR Iris Images Using Deep Learning methods to separate the gender-from-iris images even when the amount of learning data is limited, using an unsupervised stage with Restricted Boltzmann Machine (RBM) and a supervised stage using a Convolutional Neural Network (CNN).
作者: Opponent    時間: 2025-3-24 12:13

作者: ABASH    時間: 2025-3-24 16:06

作者: Mumble    時間: 2025-3-24 19:37
Book 2017ual and biometrics-related tasks. The text offers a showcase of cutting-edge research on the use of convolutional neural networks (CNN) in face, iris, fingerprint, and vascular biometric systems, in addition to surveillance systems that use soft biometrics. Issues of biometrics security are also exa
作者: 粗糙    時間: 2025-3-25 00:51
2191-6586 arning integrated biometric techniques, including face, fingThis timely text/reference presents a broad overview of advanced deep learning architectures for learning effective feature representation for perceptual and biometrics-related tasks. The text offers a showcase of cutting-edge research on t
作者: SYN    時間: 2025-3-25 06:55
2191-6586 sture-based identification, gender classification, and tattoo recognition; investigates deep learning for biometrics security, covering biometrics template protection methods, and liveness detection to protect 978-3-319-87128-8978-3-319-61657-5Series ISSN 2191-6586 Series E-ISSN 2191-6594
作者: 雇傭兵    時間: 2025-3-25 10:47
Iain C. Scotchman,L. Hywel Johnesmponent and the region-of-interest (RoI) detection component. However, far apart of that network, there are two main contributions in our proposed network that play a significant role to achieve the state-of-the-art performance in face detection. First, the multi-scale information is grouped both in
作者: Canyon    時間: 2025-3-25 13:37

作者: 爭吵加    時間: 2025-3-25 15:49

作者: impaction    時間: 2025-3-25 22:40

作者: 他一致    時間: 2025-3-26 00:34
Some Characteristics of Sandy Plaggen Soilsural networks, a deep learning framework that leverages both the spatial (depth) and temporal (optical flow) information of a video sequence. First, we evaluate the generalization performance during testing of our approach against gestures of users that have not been seen during training. Then, we s
作者: AGOG    時間: 2025-3-26 08:22
https://doi.org/10.1007/978-3-642-51349-7mugshot database with 400?K images under occlusion and low-resolution settings, compared to the one undergone traditional training. In addition, our progressively trained network is sufficiently generalized so that it can be robust to occlusions of arbitrary types and at arbitrary locations, as well
作者: GULLY    時間: 2025-3-26 08:48
https://doi.org/10.1007/978-3-642-51349-7 and detection based on deep learning. In particular, we will present deep convolutional neural network-based methods for automatic matching of tattoo images based on the AlexNet and Siamese networks. Furthermore, we will show that rather than using a simple contrastive loss function, triplet loss f
作者: Spartan    時間: 2025-3-26 16:25

作者: 演繹    時間: 2025-3-26 17:28

作者: 使饑餓    時間: 2025-3-26 21:46

作者: 取消    時間: 2025-3-27 04:12

作者: foodstuff    時間: 2025-3-27 08:39
Latent Fingerprint Image Segmentation Using Deep Neural Networkd on RBMs learns fingerprint image patches in two phases. The first phase (unsupervised pre-training) involves learning an identity mapping of the input image patches. In the second phase, fine-tuning and gradient updates are performed to minimize the cost function on the training dataset. The resul
作者: NEXUS    時間: 2025-3-27 13:13

作者: 膝蓋    時間: 2025-3-27 15:55
Iris Segmentation Using Fully Convolutional Encoder–Decoder Networksd networks, we apply a selection of conventional (non-CNN) iris segmentation algorithms on the same datasets, and similarly evaluate their performances. The results then get compared against those obtained from the FCEDNs. Based on the results, the proposed networks achieve superior performance over
作者: Gudgeon    時間: 2025-3-27 20:02

作者: 規(guī)范就好    時間: 2025-3-28 00:51

作者: overwrought    時間: 2025-3-28 04:48

作者: 有權    時間: 2025-3-28 08:53

作者: inveigh    時間: 2025-3-28 11:15
Deep Triplet Embedding Representations for Liveness Detectionfingerprints are dissimilar from the ones generated artificially. A variant of the triplet objective function is employed, that considers patches taken from two real fingerprint and a replica (or two replicas and a real fingerprint), and gives a high penalty if the distance between the matching coup
作者: Osmosis    時間: 2025-3-28 17:28

作者: staging    時間: 2025-3-28 19:41
https://doi.org/10.1007/978-3-319-61657-5Deep Learning; Face; Fingerprint; Iris; Gait; Template Protection; Anti-Spoofing; Alexnet; CNN; RBM; Biometric
作者: instill    時間: 2025-3-29 01:12
978-3-319-87128-8Springer International Publishing AG, part of Springer Nature 2017
作者: MOCK    時間: 2025-3-29 05:15
Bir Bhanu,Ajay KumarThe first dedicated work on advances in biometric identification capabilities using deep learning techniques.Covers a broad range of deep learning integrated biometric techniques, including face, fing
作者: 排斥    時間: 2025-3-29 07:55
Advances in Computer Vision and Pattern Recognitionhttp://image.papertrans.cn/d/image/264602.jpg
作者: commodity    時間: 2025-3-29 15:03
Springer Series in Physical Environmentanalysis between present deep neural network architectures for biometrics and neural architectures in the human brain is necessary for developing artificial systems with human abilities.Neuroimaging research has advanced our understanding regarding the functional architecture of the human ventral fa
作者: 消極詞匯    時間: 2025-3-29 19:33
https://doi.org/10.1007/978-1-4613-8988-0 the multiple convolution architecture and finally learn the output hashing transform via new Boosted Hashing Forest (BHF) technique. This BHF generalizes the Boosted Similarity Sensitive Coding (SSC) approach for hashing learning with joint optimization of face verification and identification. CNHF
作者: Frequency    時間: 2025-3-29 20:32

作者: GONG    時間: 2025-3-30 00:14
John E. Clark,Gerald E. ReinsonRBMs), and uses it to perform segmentation of latent fingerprint images. Artificial neural networks (ANN) are biologically inspired architectures that produce hierarchies of maps through learned weights or filters. Latent fingerprints are fingerprint impressions unintentionally left on surfaces at a
作者: lipoatrophy    時間: 2025-3-30 04:30
Nutrient Cycling in Sandy Beachesnal privacy and anonymity in during the identification process. The Convolutional Neural Network (CNN) has shown remarkable capability for learning biometric features that can offer robust and accurate matching. We introduce a new approach for the finger vein authentication using the CNN and supervi




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