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[null,"1.","Record Nr.",null,"UNINA9910575000403321"]
[null,null,"Autore",null,"Rotondi, Francesco <1966-    >"]
[null,null,"Titolo",null,"Com'è cambiato il mercato del lavoro in Italia e come cambierà : dialogo con sei ministri del lavoro protagonisti delle riforme degli ultimi 25 anni / Francesco Rotondi ; A. Orlando ... [et al.]"]
[null,null,"Pubbl/distr/stampa",null,"Soveria Mannelli, : Rubbettino, 2021"]
[null,null,"ISBN",null,"978-88-498-6986-6"]
[null,null,"Descrizione fisica",null,"143 p. ; 21 cm"]
[null,null,"Disciplina",null,"331.120945"]
[null,null,"Locazione",null,"FSPBC"]
[null,null,"Collocazione",null,"XI A 2924"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNISA996466223703316"]
[null,null,"Titolo",null,"Biometric Recognition [[electronic resource] ] : 10th Chinese Conference, CCBR 2015, Tianjin, China, November 13-15, 2015, Proceedings / / edited by Jinfeng Yang, Jucheng Yang, Zhenan Sun, Shiguang Shan, Weishi Zheng, Jianjiang Feng"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015"]
[null,null,"ISBN",null,"3-319-25417-0"]
[null,null,"Edizione",null,"[1st ed. 2015.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XVIII, 739 p. 377 illus. in color.)"]
[null,null,"Collana",null,"Image Processing, Computer Vision, Pattern Recognition, and Graphics ; ; 9428"]
[null,null,"Disciplina",null,"006"]
[null,null,"Soggetti",null,"Biometrics (Biology)"]
[{"$nulls":4},"Pattern recognition"]
[{"$nulls":4},"Optical data processing"]
[{"$nulls":4},"Algorithms"]
[{"$nulls":4},"Computer graphics"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Biometrics"]
[{"$nulls":4},"Pattern Recognition"]
[{"$nulls":4},"Image Processing and Computer Vision"]
[{"$nulls":4},"Algorithm Analysis and Problem Complexity"]
[{"$nulls":4},"Computer Graphics"]
[{"$nulls":4},"Information Systems Applications (incl. Internet)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents -- Face -- Adaptive Quotient Image with 3D Generic Elastic Models for Pose and Illumination Invariant Face Recognition -- 1 Introduction -- 2 Adaptive Quotient Image -- 2.1 Quotient Image -- 2.2 Adaptive Quotient Image -- 3 3D Face Reconstruction by GEM -- 4 Face Recognition via Pose-Specific Metric -- 4.1 Pose Estimation and Alignment -- 4.2 Recognition via Pose-Specific Metric -- 5 Experiments and Results -- 5.1 Results -- 6 Conclusion -- References -- Low Rank Analysis of Eye Image Sequence - A Novel Basis for Face Liveness Detection -- 1 Introduction -- 2 Motivations -- 3 Proposed Method -- 3.1 Sample "]
[{"$nulls":4},"Noising Model -- 3.2 Solutions of the Noising Model -- 3.3 Basis for Classification -- 3.4 The Proposed Algorithm -- 4 Experiments -- 5 Conclusion -- References -- Non-negative Compatible Kernel Construction for Face Recognition -- 1 Introduction -- 2 Nonnegative Compatible Kernel Construction -- 2.1 Symmetric NMF -- 2.2 Nonnegative Interpolatory Basis Function Construction -- 2.3 Nonnegative Compatible Kernel Construction -- 3 Experimental Results -- 3.1 Experiments on ORL Database -- 3.2 Experiments on Pain Expression Database -- 4 Conclusions -- References -- 3D Face Recognition Using Local Features Matching on Sphere Depth Representation -- 1 Introduction -- 1.1 Related Work -- 1.2 Motivation and Approach Overview -- 2 Generation of Sphere Depth Image -- 3 Local Feature Extraction on Sphere Depth Image -- 3.1 Problem in Keypoints Selection -- 3.2 Ranking Keypoints in Keypoints Selection -- 4 Experiment Analysis -- 4.1 Experiment on Ran nking Model -- 4.2 Experiment on Pose Change 3D Faces Images -- 5 Conclusion -- References -- Face Recognition Using Local PCA Filters -- 1 Introduction -- 2 Method -- 2.1 Filter Learning -- 2.2 Feature Coding -- 3 Experiment -- 3.1 Experiment on Feret Database."]
[{"$nulls":4},"3.2 Experiment on LFW Database -- 4 Conclusion -- References -- Block Statistical Features-based Face Verification on Second Generation Identity Card -- 1 Introduction -- 2 Face Representation Based on LGBP -- 3 The Proposed Algorithm -- 3.1 Face Presentation Based on BSF -- 3.2 Energy Check on Gabor Filter -- 3.3 Face Verification Based on BSF -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Experiment with NEU-ID Database -- 5 Conclusion and Discussion -- References -- Towards Practical Face Recognition: A Local Binary Pattern Non Frontal Faces Filtering Approach -- 1 Introduction -- 2 Overall Design Framework -- 2.1 Face Detection -- 2.2 LBP Feature Extraction -- 3 Experiment -- 3.1 Establish Facial Pose Database -- 3.2 Experimental Procedure and Results -- 4 Summary and Prospect -- References -- Metric Learning Based False Positives Filtering for Face Detection -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Experiments on Our Wild Dataset -- 4.3 Experiments on FDDB -- 5 Conclusion -- References -- Face Recognition via Compact Fisher Vector -- 1 Introduction -- 2 Fisher Vector and Related Encoding Strategies -- 2.1 Fisher Kernel and Fisher Vector -- 2.2 Fisher Vector Normalization -- 2.3 Integrating Spatial Information -- 2.4 VLAD and Intra-Normalization -- 3 Compact Fisher Vector Representation -- 3.1 Sparsifying Fisher Vector -- 3.2 Fisher Vector with First Order Statistically Only -- 3.3 Residual Normalization -- 3.4 Tweaking Fisher Vector Representation -- 3.5 Normalization -- 4 Experiments -- 4.1 FERET -- 4.2 Labeled Faces in the Wild (LFW) -- 5 Conclusion -- References -- Nonlinear Metric Learning with Deep Convolutional Neural Network for Face Verification -- 1 Introduction -- 2 Related Work -- 2.1 Similarity Distance Metric Learning."]
[{"$nulls":4},"2.2 Deep Learning and Convolutional Neural Network -- 3 Proposed Method -- 3.1 Nonlinear Metric Learning with Deep ConvNet -- 3.2 Discrimination Similarity Distance Metric with Deep ConvNets -- 3.3 Implementation Details -- 4 Preliminary Experiment -- 4.1 Datasets and Experimental Settings -- 4.2 Comparison with Existing Deep Metric Learning Methods -- 4.3 Comparison with State-of-the-Art Methods -- 5 Conclusion -- References -- Locally Collaborative Representation in SimilarSubspace for Face Recognition -- 1 Introduction -- 2 Sparse Representation and Collaborative Representation -- 2.1 Sparse Representation Based Classification (SRC) -- 2.2 Collaborative Representation Based Classification (CRC) -- 3 Locally Collaborative "]
[{"$nulls":4},"Representation Based Classification -- 4 Experimental Results -- 5 Conclusion and Discussion -- References -- A DCNN and SDM Based Face Alignment Algorithm -- 1 Introduction -- 2 Coarsely Localize 5 Landmarks Based on DCNN -- 3 Finely Localize 68 Landmarks Based on SDM -- 3.1 Initialization -- 3.2 Finetune Landmarks -- 4 Experiments and Analysis -- 5 Conclusion -- References -- Robust Face Detection Based on Enhanced Local Sensitive Support Vector Machine -- 1 Introduction -- 2 Background: LSSVM -- 2.1 Discussion -- 3 Proposed Method -- 3.1 The Adaboost Based Background Filter -- 3.2 Locality-Sensitive SVM Using Kernel Combination -- 4 Experiments -- 4.1 Evaluation on CMU+MIT Dataset -- 4.2 Evaluation on FDDB Dataset -- 5 Conclusions -- References -- An Efficient Non-negative Matrix Factorization with Its Application to Face Recognition -- 1 Introduction -- 2 Traditional NMF -- 3 The Proposed NMF -- 4 Experimental Results -- 4.1 Comparisons on Convergence -- 4.2 Comparisons on Performance -- 5 Conclusions -- References -- Patch-based Sparse Dictionary Representation for Face Recognition with Single Sample per Person."]
[{"$nulls":4},"1 Introduction -- 2 Related Work -- 3 Our Proposed Method -- 4 Classification -- 5 Experiment -- 6 Conclusion -- References -- Non-negative Sparsity Preserving Projections Algorithm Based Face Recognition -- 1 Introduction -- 2 Algorithm Overview -- 2.1 Locality Preserving Projections -- 2.2 Non-negative Sparsity Preserving Projections -- 3 Experiments -- 3.1 Experiments on RL OR Face Database -- 3.2 Experiments on FERET Face Database -- 3.3 Experimental Analysis -- 4 Conclusions -- References -- WLD-TOP Based Algorithm against Face Spoofing Attacks -- 1 Introduction -- 2 Related Work -- 3 WLD from Three Orthogonal Planes (WLD-TOP) for Image Representation -- 3.1 Modified WLD -- 3.2 WLD-TOP -- 4 Experiments -- 4.1 Data Set -- 4.2 Results on the Intra-database -- 4.3 Results on the Cross-Database -- 4.4 Effectiveness of Each WLD-TOP Plane -- 5 Conclusion -- References -- Heterogeneous Face Recognition Based on Super Resolution Reconstruction by Adaptive Multi-dictionary Learning -- 1 Introduction -- 2 Sketch-to-Photo Transformation -- 3 Super-Resolution of Synthesized Photos -- 3.1 Super Resolution Reconstruction Based on Sparse Representation -- 3.2 Adaptive Multi-dictionary Learning -- 3.3 Training Samples Clustering -- 3.4 Multi-dictionary Learning -- 3.5 Super Resolution Reconstruction Model -- 4 Face Recognition Based on 2DMFA -- 4.1 Marginal Fisher Analysis -- 4.2 Two-Dimensional Marginal Fisher Analysis -- 5 Experiments -- 6 Conclusion -- References -- 3D Face Recognition Fusing Spherical Depth Map and Spherical Texture Map -- 1 Introduction -- 2 Pure Face Extraction -- 3 Recognition Process -- 3.1 Spherical Depth Map and Spherical Texture Map -- 3.2 Sparse Representation -- 4 Experiments -- 4.1 Database -- 4.2 Recognition -- 5 Conclusion -- References -- Privacy Preserving Face Identification in the Cloud through Sparse Representation."]
[{"$nulls":4},"1 Introduction -- 2 Background -- 2.1 Cryptography Primitives -- 2.2 SCiFI Overview -- 3 Privacy Preserving Face Identification -- 3.1 Modified Sparse Representation Based Face Identification -- 3.2 Private Face Identification Protocol -- 4 Experimental Results -- 5 Conclusion and Discussion -- References -- Infrared Face Recognition Based on ODP of Local Binary Patterns -- 1 Introduction -- 2 Discriminative Patterns Based on Local Binary Patterns -- 3 Optimized Discriminative Patterns (ODP) of LBP -- 4 The Multi-classifier Based on Voting Mechanism -- 5 Experiment Results -- 6 Conclusions -- References -- Image Classification Based on Discriminative Dictionary Pair Learning -- 1 Introduction -- 2 Discriminative Dictionary Pair Learning -- 3 "]
[{"$nulls":4},"Optimization -- 4 Classification Scheme -- 5 Experiments -- 5.1 Face Recognition -- 5.2 Handwritten Digit Recognition -- 6 Conclusion -- References -- Weber Local Gradient Pattern (WLGP) Method for Face Recognition -- Introduction -- 2 Proposed Method -- 2.1 Weber Local Descriptor -- 2.2 Proposed WLGD -- 3 Experimental Results -- 3.1 Experiments on ORL Database -- 3.2 Experiments on Infrared Face Database -- 4 Conclusion -- References -- Multi-task Attribute Joint Feature Learning -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 4 Experimental Protocol and Results Analysis -- 4.1 Experiment Results and Discussion -- 4.2 Experiment Results and Discussion -- 5 Conclusion -- References -- Person-specific Face Spoofing Detection for Replay Attack Based on Gaze Estimation -- 1 Introduction -- 2 Proposed Face Spoofing Detection Method -- 2.1 Gaze Estimation -- Gaze Feature Extraction. -- Adaptive Linear Regression with Incremental Learning. -- 2.2 Liveness Judgement -- 3 Experiments -- 3.1 Database -- 3.2 Experimental Results -- Effectiveness of Incremental Learning."]
[{"$nulls":4},"Effectiveness of Proposed Face Spoofing Detection Method."]
[null,null,null,"Sommario/riassunto","This book constitutes the refereed proceedings of the 10th Chinese Conference on Biometric Recognition, CCBR 2015, held in Tianjin, China, in November 2015. The 85 revised full papers presented were carefully reviewed and selected from among 120 submissions. The papers focus on face, fingerprint and palmprint, vein biometrics, iris and ocular biometrics, behavioral biometrics, application and system of biometrics, multi-biometrics and information fusion, other biometric recognition and processing.  ."]
[null,"3.","Record Nr.",null,"UNINA9910150161803321"]
[null,null,"Autore",null,"Miller Ingrid Loos"]
[null,null,"Titolo",null,"Weight management for triathletes : when training is not enough / / Ingrid Loos Miller"]
[null,null,"Pubbl/distr/stampa",null,"Aachen, [Germany] : , : Meyer & Meyer Sport, , 2016"]
[{"$nulls":4},"©2016"]
[null,null,"ISBN",null,"1-78255-757-1"]
[null,null,"Descrizione fisica",null,"1 online resource (156 pages) : color illustrations"]
[null,null,"Disciplina",null,"613.2024796"]
[null,null,"Soggetti",null,"Triathletes - Nutrition"]
[{"$nulls":4},"Triathlon - Training"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]