作者: aqueduct 時(shí)間: 2025-3-21 23:33 作者: emission 時(shí)間: 2025-3-22 03:18
Muhammad Hasan Amara,Abd Al-Rahman Mar’I performance is largely influenced by block generation and post-processing, concealing the performance of face classification core module. Secondly, implementing and optimizing all the three steps results in a very heavy work, which is a big barrier for researchers who only cares about classificatio作者: Paleontology 時(shí)間: 2025-3-22 05:14 作者: ALT 時(shí)間: 2025-3-22 11:57
Muhammad Hasan Amara,Abd Al-Rahman Mar’Iand estimates 2D and 3D facial feature points simultaneously. In stage one, 2D and 3D facial feature points are roughly detected on the input face image, and head pose analysis is applied based on the 3D facial feature points to estimate its head pose. The face is then classified into one of three c作者: Recessive 時(shí)間: 2025-3-22 12:58
Language Attitudes and Ideologies,to large variations due to differences in expressions and pose. Unlike previous shape regression-based approaches, we propose to reference features weighted by three different face landmarks, which are much more robust to shape variations. Then, a correlation-based feature selection method and a two作者: CLIFF 時(shí)間: 2025-3-22 20:28
https://doi.org/10.1007/0-306-47588-Xrification, face detection and face alignment. However, face alignment remains a challenging problem due to large pose variation and the lack of data. Although researchers have designed various network architecture to handle this problem, pose information was rarely used explicitly. In this paper, w作者: Assault 時(shí)間: 2025-3-22 23:46
https://doi.org/10.1007/0-306-47588-Xolutional Network (TCDCN), which are complicated and difficult to train. To solve this problem, this paper proposes a new Single Deep CNN (SDN). Unlike cascaded CNNs, SDN stacks three layer groups: each group consists of two convolutional layers and a max-pooling layer. This network structure can ex作者: enfeeble 時(shí)間: 2025-3-23 05:14 作者: 錯(cuò)誤 時(shí)間: 2025-3-23 08:06
https://doi.org/10.1007/0-306-47588-Xo use different landmarks of faces to solve the problems caused by poses. In order to increase the ability of verification, semi-verification signal is used for training one network. The final face representation is formed by catenating features of two deep CNNs after PCA reduction. What’s more, eac作者: Accomplish 時(shí)間: 2025-3-23 13:40
Yasemin K?rkg?z,Carol Griffithsby illumination and occlusion. Motivated by convolutional architecture of deep learning and the advantages of KMeans algorithm in filters learning. In this paper, a simple yet effective face recognition approach is proposed, which consists of three components: convolutional filters learning, nonline作者: 混合 時(shí)間: 2025-3-23 16:54 作者: 不如屎殼郎 時(shí)間: 2025-3-23 18:19 作者: condescend 時(shí)間: 2025-3-24 00:27
https://doi.org/10.1007/978-3-030-74958-3 novel low rank and weighted sparse graph. First, we utilize exact low rank representation by the nuclear norm and Forbenius norm to capture the global subspace structure. Meanwhile, we build the weighted sparse regularization term with shape interaction information to capture the local linear struc作者: Obituary 時(shí)間: 2025-3-24 04:10 作者: 減弱不好 時(shí)間: 2025-3-24 07:55 作者: gerrymander 時(shí)間: 2025-3-24 12:32
Neural Underpinnings of Semantic Processingral face recognition methods dealing with visible light images are unqualified. Cross-domain face recognition refers to a series of methods in response to face recognition problems whose inputs may come from multiple modalities, such as visible light images, sketch, near infrared images, 3D data, lo作者: 分散 時(shí)間: 2025-3-24 18:02 作者: fetter 時(shí)間: 2025-3-24 21:23 作者: 半圓鑿 時(shí)間: 2025-3-25 00:38 作者: xanthelasma 時(shí)間: 2025-3-25 05:02 作者: enfeeble 時(shí)間: 2025-3-25 11:31 作者: Monolithic 時(shí)間: 2025-3-25 12:03 作者: 泰然自若 時(shí)間: 2025-3-25 17:31
978-3-319-46653-8Springer International Publishing AG 2016作者: 全國性 時(shí)間: 2025-3-25 23:54 作者: 希望 時(shí)間: 2025-3-26 01:25
Occlusion-Robust Face Detection Using Shallow and Deep Proposal Based Faster R-CNNe detectors cannot fulfill the requirements in real-world scenarios especially in the presence of severe occlusions. This paper proposes a novel and effective approach to occlusion-robust face detection. It combines two major phases, . proposal generation and classification. In the former, we combin作者: Bravado 時(shí)間: 2025-3-26 05:32 作者: CORD 時(shí)間: 2025-3-26 11:53 作者: 吸引力 時(shí)間: 2025-3-26 13:19
Binary Classifiers and Radial Symmetry Transform for Fast and Accurate Eye Localizationon. The algorithm is based on binary classifiers and fast radial symmetry transform. First, eye candidates can be detected by the fast radial symmetry transform in infrared image. Then three-stage binary classifiers are used to eliminate most unreliable eye candidates. Finally, the mean eye template作者: JAMB 時(shí)間: 2025-3-26 17:19
Robust Multi-view Face Alignment Based on Cascaded 2D/3D Face Shape Regressionand estimates 2D and 3D facial feature points simultaneously. In stage one, 2D and 3D facial feature points are roughly detected on the input face image, and head pose analysis is applied based on the 3D facial feature points to estimate its head pose. The face is then classified into one of three c作者: nerve-sparing 時(shí)間: 2025-3-26 20:59 作者: 過多 時(shí)間: 2025-3-27 03:23
Pose Aided Deep Convolutional Neural Networks for Face Alignmentrification, face detection and face alignment. However, face alignment remains a challenging problem due to large pose variation and the lack of data. Although researchers have designed various network architecture to handle this problem, pose information was rarely used explicitly. In this paper, w作者: 相反放置 時(shí)間: 2025-3-27 07:19
Face Landmark Localization Using a Single Deep Networkolutional Network (TCDCN), which are complicated and difficult to train. To solve this problem, this paper proposes a new Single Deep CNN (SDN). Unlike cascaded CNNs, SDN stacks three layer groups: each group consists of two convolutional layers and a max-pooling layer. This network structure can ex作者: Mechanics 時(shí)間: 2025-3-27 09:38
Cascaded Regression for 3D Face Alignmentted by face shape deformations and poor light conditions. With the assist of extra shape information provided by 3D facial model, these difficulties can be eased to some degree. In this paper, we propose 3D Cascaded Regression for detecting facial landmarks on 3D faces. Our algorithm makes full use 作者: 易于交談 時(shí)間: 2025-3-27 15:30
Deep CNNs for Face Verificationo use different landmarks of faces to solve the problems caused by poses. In order to increase the ability of verification, semi-verification signal is used for training one network. The final face representation is formed by catenating features of two deep CNNs after PCA reduction. What’s more, eac作者: probate 時(shí)間: 2025-3-27 17:52 作者: 護(hù)身符 時(shí)間: 2025-3-27 23:52 作者: 供過于求 時(shí)間: 2025-3-28 05:12 作者: Bother 時(shí)間: 2025-3-28 09:06
A Semi-supervised Learning Algorithm Based on Low Rank and Weighted Sparse Graph for Face Recognitio novel low rank and weighted sparse graph. First, we utilize exact low rank representation by the nuclear norm and Forbenius norm to capture the global subspace structure. Meanwhile, we build the weighted sparse regularization term with shape interaction information to capture the local linear struc作者: GENRE 時(shí)間: 2025-3-28 11:32
Multilinear Local Fisher Discriminant Analysis for Face Recognitiontion. MLFDA achieves feature extraction by finding a multilinear projection to map the original tensor space into a tensor subspace that maximize the local between-class scatter as well as minimize the local within-class scatter. The experimental result shows that MLFDA has an outperformance.作者: debouch 時(shí)間: 2025-3-28 17:13
Combining Multiple Features for Cross-Domain Face Sketch Recognitionan intra-modality method called the Eigentransformation and two inter-modality methods based on modality invariant features, namely the Multiscale Local Binary Pattern (MLBP) and the Histogram of Averaged Orientation Gradients (HAOG). Meanwhile, a sum-score fusion of min-max normalized scores is app作者: 幾何學(xué)家 時(shí)間: 2025-3-28 19:02 作者: browbeat 時(shí)間: 2025-3-29 01:58
Exploring Deep Features with Different Distance Measures for Still to Video Face Matchinge often captured with high quality and cooperative user condition. On the contrary, video clips usually show more variations and of low quality. In this paper, we primarily focus on the S2V face recognition where face gallery is formed by a few still face images, and the query is the video clip. We 作者: GROSS 時(shí)間: 2025-3-29 04:27
0302-9743 BR 2016, held in Chengdu, China, in October 2016...The 84 revised full papers presented in this book were carefully reviewed and selected from 138 submissions. The papers focus on Face Recognition and Analysis; Fingerprint, Palm-print and Vascular Biometrics; Iris and Ocular Biometrics; Behavioral B作者: bizarre 時(shí)間: 2025-3-29 10:51 作者: Expand 時(shí)間: 2025-3-29 11:42
Compact Face Representation via Forward Model Selectionl selection algorithm is designed to simultaneously select the complementary face models and generate the compact representation. Employing a public dataset as training set and fusing by only six selected face networks, the recognition system with this compact face representation achieves 99.05?% accuracy on LFW benchmark.作者: 設(shè)施 時(shí)間: 2025-3-29 17:42 作者: Oligarchy 時(shí)間: 2025-3-29 20:24
https://doi.org/10.1007/0-306-47588-X is employed to identify the real eyes from the reliable eye candidates. A large number of tests have been completed to verify the performance of the proposed algorithm. Experimental results demonstrate that the algorithm proposed in this article is robust and efficient.作者: Yag-Capsulotomy 時(shí)間: 2025-3-30 02:26
Language Attitudes and Ideologies,-level boosted regression are applied to establish accurate relation between features and shapes. Experiments on two challenging face datasets (LFPW, COFW) show that our proposed approach significantly outperforms the state-of-art in terms of both efficiency and accuracy.作者: Celiac-Plexus 時(shí)間: 2025-3-30 04:04
https://doi.org/10.1007/0-306-47588-Xtract more global high-level features, which express the face landmarks more precisely. Extensive experiments show that SDN outperforms existing DCNN methods and is robust to large pose variation, lighting and even severe occlusion. While the network complexity is also reduced obviously compared to other methods.作者: HPA533 時(shí)間: 2025-3-30 09:21 作者: 入伍儀式 時(shí)間: 2025-3-30 13:33
Binary Classifiers and Radial Symmetry Transform for Fast and Accurate Eye Localization is employed to identify the real eyes from the reliable eye candidates. A large number of tests have been completed to verify the performance of the proposed algorithm. Experimental results demonstrate that the algorithm proposed in this article is robust and efficient.作者: Intersect 時(shí)間: 2025-3-30 16:49
Extended Robust Cascaded Pose Regression for Face Alignment-level boosted regression are applied to establish accurate relation between features and shapes. Experiments on two challenging face datasets (LFPW, COFW) show that our proposed approach significantly outperforms the state-of-art in terms of both efficiency and accuracy.作者: 寡頭政治 時(shí)間: 2025-3-30 23:46 作者: Gleason-score 時(shí)間: 2025-3-31 02:01 作者: induct 時(shí)間: 2025-3-31 05:55
0302-9743 iometrics; Affective Computing; Feature Extraction and Classification Theory; Anti-Spoofing and Privacy; Surveillance; and DNA and Emerging Biometrics..978-3-319-46653-8978-3-319-46654-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: incredulity 時(shí)間: 2025-3-31 11:18
Muhammad Hasan Amara,Abd Al-Rahman Mar’Imprehensive set of candidate regions. In the latter, we further decide whether the regions are faces using a well-trained Faster R-CNN. Experiments are conducted on the WIDER FACE benchmark, and the results clearly prove the competency of the proposed method at detecting occluded faces.作者: LITHE 時(shí)間: 2025-3-31 16:32 作者: 表主動 時(shí)間: 2025-3-31 19:15 作者: 慢跑鞋 時(shí)間: 2025-4-1 00:57
https://doi.org/10.1007/978-3-030-74958-3cted by an effective post-processing method. We evaluate the proposed method by performing semi-supervised classification experiments on ORL, Extended Yale B and AR face database. The experimental results show that our approach improves the accuracy of semi-supervised learning and achieves the state-of-the-art performance.作者: Carcinoma 時(shí)間: 2025-4-1 03:15 作者: 不能逃避 時(shí)間: 2025-4-1 06:47 作者: Omniscient 時(shí)間: 2025-4-1 13:56
Deep CNNs for Face Verificationace representation from one region and one resolution of a face jointing Joint Bayesian classifier. Experiments show that our method can extract effective face representation and our algorithm achieves 99.71?% verification accuracy on LFW dataset.