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