標(biāo)題: Titlebook: Deep Learning-Based Face Analytics; Nalini K Ratha,Vishal M. Patel,Rama Chellappa Book 2021 The Editor(s) (if applicable) and The Author(s [打印本頁] 作者: ALLY 時(shí)間: 2025-3-21 16:47
書目名稱Deep Learning-Based Face Analytics影響因子(影響力)
書目名稱Deep Learning-Based Face Analytics影響因子(影響力)學(xué)科排名
書目名稱Deep Learning-Based Face Analytics網(wǎng)絡(luò)公開度
書目名稱Deep Learning-Based Face Analytics網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Deep Learning-Based Face Analytics被引頻次
書目名稱Deep Learning-Based Face Analytics被引頻次學(xué)科排名
書目名稱Deep Learning-Based Face Analytics年度引用
書目名稱Deep Learning-Based Face Analytics年度引用學(xué)科排名
書目名稱Deep Learning-Based Face Analytics讀者反饋
書目名稱Deep Learning-Based Face Analytics讀者反饋學(xué)科排名
作者: 陶器 時(shí)間: 2025-3-21 21:26 作者: mucous-membrane 時(shí)間: 2025-3-22 02:32 作者: vertebrate 時(shí)間: 2025-3-22 07:58
https://doi.org/10.1007/978-3-642-95770-3with only 8% labeling, we can achieve performance very close to that with full-set labeling. In the second problem, we focus on the size of the camera network?and consider how to onboard new cameras?into an existing network with little to no additional supervision. We leverage upon transfer learning作者: 本土 時(shí)間: 2025-3-22 10:15
Zivil-milit?rische Zusammenarbeitgorithms. The two methods reach different conclusions. While the observational method reports gender and skin color biases, the experimental method?reveals biases due to gender, hair length, age, and facial hair. We also show that our synthetic transects allow for a more straightforward bias analysi作者: incisive 時(shí)間: 2025-3-22 16:03 作者: incisive 時(shí)間: 2025-3-22 17:36 作者: intimate 時(shí)間: 2025-3-22 21:19 作者: Prostatism 時(shí)間: 2025-3-23 04:11 作者: lethargy 時(shí)間: 2025-3-23 05:49 作者: 詞匯表 時(shí)間: 2025-3-23 10:54
https://doi.org/10.1007/978-3-662-33316-7ure of the face image is performed on a mobile device or in a separate location from the face verification?or search process, the amount of data that needs to be transmitted over the network should be minimized.作者: 健壯 時(shí)間: 2025-3-23 13:51 作者: 平項(xiàng)山 時(shí)間: 2025-3-23 18:04
https://doi.org/10.1007/978-3-658-41970-7in how humans with various levels of expertise approach face identification tasks. We conclude by considering the challenging problem of human and machine performance on recognition of faces of different races. Understanding how humans and machines perform these tasks can lead to more effective and accurate face recognition in applied settings.作者: CANT 時(shí)間: 2025-3-24 01:46
Book 2021methods based on autoencoders, restricted Boltzmann machines, and deep convolutional neural networks for face detection, localization, tracking, recognition, etc. The authors also discuss merits and drawbacks of available approaches and identifies promising avenues of research in this rapidly evolvi作者: 漂亮 時(shí)間: 2025-3-24 05:06
Empfindsamkeit und Sturm und Drang,w born face recognition. Finally, we evaluate and compare these techniques. Our comparative analysis shows that the state-of-the-art SSF-CNN technique achieves an average of rank-1 new born accuracy of ..作者: 傷心 時(shí)間: 2025-3-24 08:41 作者: 乳汁 時(shí)間: 2025-3-24 13:07 作者: Myocyte 時(shí)間: 2025-3-24 17:17
Thermal-to-Visible Face Synthesis and Recognition,ents in face recognition accuracy, particularly in unconstrained scenarios?[., ., ., ., .]. Also, largely driven by social network companies, progress in face recognition research, development, and deployment have focused on faces collected in visible regimes of the electromagnetic spectrum.作者: 巫婆 時(shí)間: 2025-3-24 19:49 作者: 不愿 時(shí)間: 2025-3-25 00:09
Obstructing DeepFakes by Disrupting Face Detection and Facial Landmarks Extraction,action?method with specially designed imperceptible adversarial perturbations?to reduce the quality of the detected faces. We empirically show the effectiveness of our methods in disrupting state-of-the-art DNN-based face detectors and facial landmark extractors on several datasets.作者: Orthodontics 時(shí)間: 2025-3-25 03:58 作者: Chronological 時(shí)間: 2025-3-25 08:10 作者: 津貼 時(shí)間: 2025-3-25 14:40 作者: diabetes 時(shí)間: 2025-3-25 19:29
2191-6586 thods.Explores on Deepfake attacks in face recognition.Compa.This book provides an overview of different deep learning-based methods for face recognition and related problems. Specifically, the authors present methods based on autoencoders, restricted Boltzmann machines, and deep convolutional neura作者: gimmick 時(shí)間: 2025-3-25 20:10 作者: 興奮過度 時(shí)間: 2025-3-26 03:22
978-3-030-74699-5The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: Firefly 時(shí)間: 2025-3-26 07:51
Deutsche Au?enpolitik und Krisenpr?ventionThe goal?of every contemporary recognition approach is to learn robust and unambiguous object representations in feature space. These learned powerful disentangled representations make it possible to build effective classifiers and are an active research topic in many fields such as face analytics.作者: CREST 時(shí)間: 2025-3-26 09:14 作者: Buttress 時(shí)間: 2025-3-26 16:20 作者: 實(shí)現(xiàn) 時(shí)間: 2025-3-26 18:08 作者: cajole 時(shí)間: 2025-3-26 22:34
Deutsche Au?enwirtschaftsf?rderungt of the progress in automatic face recognition has been driven by deep networks in the past few years. In this article, we provide an overview of recent progress in this area and discuss state-of-the-art CNN-based face recognition and verification systems. We also present some open questions and di作者: 引起痛苦 時(shí)間: 2025-3-27 05:06 作者: 關(guān)節(jié)炎 時(shí)間: 2025-3-27 08:34
https://doi.org/10.1007/978-3-658-26020-0age synthesis. Conventional 3DMM?is learned from a set of 3D face scans with associated well-controlled 2D face images, and represented by two sets of PCA basis functions. Due to the type and amount of training data, as well as, the linear bases, the representation power of 3DMM?can be limited.作者: 向前變橢圓 時(shí)間: 2025-3-27 10:18
https://doi.org/10.1007/978-3-662-33316-7cial to leverage additional properties of the data to successfully recover the lost facial details in the deblurred image. Priors such as sparsity [., ., .], low-rank [.], manifold [.], and patch similarity [.] have been proposed in the literature to obtain a regularized solution.作者: 癡呆 時(shí)間: 2025-3-27 17:36
https://doi.org/10.1007/978-3-662-33316-7l-valued feature, often obtained using a deep network. However, comparisons of this high-dimensional feature can be computationally expensive. Furthermore, when dealing with large face images database this representation can lead to prohibitive storage requirements. Also, in a context where the capt作者: Sputum 時(shí)間: 2025-3-27 20:24
Empfindsamkeit und Sturm und Drang,e extremely useful. With the help of the right biometric system in place, cases of swapping, for instance, can be evaluated much faster. In this chapter, we first discuss the various biometric modalities along with their advantages and limitations. We next discuss the face biometrics in detail and p作者: incarcerate 時(shí)間: 2025-3-28 01:52 作者: kyphoplasty 時(shí)間: 2025-3-28 03:06 作者: 命令變成大炮 時(shí)間: 2025-3-28 10:19
Darstellung des Untersuchungsmodells,t subject. Various face recognition (FR) systems have been developed over the last two decades. Recent advances in machine learning and computer vision methods have provided robust systems that achieve significant gains in performance of face recognition systems [., .]. Deep learning methods, enable作者: 糾纏,纏繞 時(shí)間: 2025-3-28 14:30 作者: 馬籠頭 時(shí)間: 2025-3-28 15:31
https://doi.org/10.1007/978-3-642-95770-3reat. Although Presentation Attack Detection (PAD) methods attempt to remedy this problem, often they fail in generalizing to unseen attacks and environments. As the quality of presentation attack instruments improves over time, achieving reliable PA detection using only visual spectra remains a maj作者: Humble 時(shí)間: 2025-3-28 19:57
https://doi.org/10.1007/978-3-642-95770-3ltiple cameras. It is an extremely active area of research today. Most of the approaches are extensively supervised, in the sense that they require significant labeling effort to train re-identification models, usually based on deep networks. However, as in other problems in computer vision, it rais作者: Ganglion-Cyst 時(shí)間: 2025-3-29 00:22
Zivil-milit?rische Zusammenarbeit in computer vision are based on . datasets and so conflate algorithmic bias with dataset bias. To address this problem, we develop an . method for measuring algorithmic bias of face analysis?algorithms, which directly manipulates the attributes of interest, e.g., gender and skin tone, in order to r作者: heterogeneous 時(shí)間: 2025-3-29 04:08 作者: Lament 時(shí)間: 2025-3-29 09:53
Frieden als Referenzdimension der SDGss due to the confluence of several factors, primarily the development of advanced machine learning algorithms, free and robust software implementations thereof, ever faster GPU processors for running them, vast web-scraped face image databases, open performance?benchmarks, and a vibrant face recogni作者: 令人不快 時(shí)間: 2025-3-29 15:21
Deep Learning-Based Face Analytics978-3-030-74697-1Series ISSN 2191-6586 Series E-ISSN 2191-6594 作者: 一窩小鳥 時(shí)間: 2025-3-29 17:21 作者: Buttress 時(shí)間: 2025-3-29 22:16 作者: Obscure 時(shí)間: 2025-3-30 02:15
https://doi.org/10.1007/978-3-662-33316-7cial to leverage additional properties of the data to successfully recover the lost facial details in the deblurred image. Priors such as sparsity [., ., .], low-rank [.], manifold [.], and patch similarity [.] have been proposed in the literature to obtain a regularized solution.作者: Substitution 時(shí)間: 2025-3-30 04:16 作者: vasospasm 時(shí)間: 2025-3-30 09:01
Frieden als Referenzdimension der SDGss due to the confluence of several factors, primarily the development of advanced machine learning algorithms, free and robust software implementations thereof, ever faster GPU processors for running them, vast web-scraped face image databases, open performance?benchmarks, and a vibrant face recognition literature.作者: 加強(qiáng)防衛(wèi) 時(shí)間: 2025-3-30 15:49
Nalini K Ratha,Vishal M. Patel,Rama ChellappaIs First compiled source on deep learning applied to face image and video analytics.Reflects on Bias in face analytic algorithms using AI methods.Explores on Deepfake attacks in face recognition.Compa作者: Ingenuity 時(shí)間: 2025-3-30 17:07
Advances in Computer Vision and Pattern Recognitionhttp://image.papertrans.cn/d/image/264640.jpg作者: 抱怨 時(shí)間: 2025-3-30 21:00 作者: Highbrow 時(shí)間: 2025-3-31 03:52 作者: ARY 時(shí)間: 2025-3-31 08:23 作者: 搖擺 時(shí)間: 2025-3-31 10:14
Deep Feature Fusion for Face Analytics, raw data, (ii) .—which process the raw data from sensors to generate features representing the original data, and (iii) .- –which produce a score or a measure that conveys the likelihood of the provided features belonging to an application specific hypothesis.作者: 無節(jié)奏 時(shí)間: 2025-3-31 13:32 作者: 畏縮 時(shí)間: 2025-3-31 18:36
