Intro; Preface; Organization; Contents -- Part II; Face and Posture Analysis; Projective Representation Learning for Discriminative Face Recognition; 1 Introduction; 2 Related Works; 2.1 l2 Regularization-Based Representation; 2.2 Linear Discriminant Analysis; 3 The Proposed Method; 3.1 Face Projection; 3.2 Face Representation; 3.3 Classification; 3.4 Computational Complexity Analysis; 4 Experimental Results; 4.1 Dimension and Parameter Selections; 4.2 Experiments on the FERET Face Dataset; 4.3 Experiments on the Extended Yale B Face Dataset; 4.4 Experiments on the CMU Multi-PIE Face Dataset
5 ConclusionReferences; Improved Face Verification with Simple Weighted Feature Combination; 1 Introduction; 2 Related Work; 3 The Proposed Method; 3.1 Data Pre-processing; 3.2 CB-VGG Architecture; 3.3 Weighted Average Features; 3.4 LMNN Metric Learning; 4 Experiments; 4.1 Details of Training Stage; 4.2 Results of Test Datasets; 4.3 Analysis and Discussion; 5 Conclusions; References; Pore-Scale Facial Features Matching Under 3D Morphable Model Constraint; 1 Introduction; 2 Pore-Scale Invariant Feature Transform; 2.1 Pore-Scale Feature Detection; 2.2 Pore-Scale Feature Descriptor
3 Matching with the 3D Morphable Model Constraint3.1 3D Morphable Model; 3.2 3D Dense Face Alignment; 3.3 3D Morphable Model Constraint; 4 Experiment; 4.1 Skin Matching Based on the Bosphorus Dataset; 4.2 Pore-to-pore Correspondences Dataset; 5 Conclusion; References; Combination of Pyramid CNN Representation and Spatial-Temporal Representation for Facial Expression Recognition; 1 Introduction; 2 Methodology; 2.1 Static Pyramid CNN-based Feature; 2.2 LBP-TOP Feature; 3 Experimental Results; 3.1 Dataset; 3.2 Experimental Results on CK+ Dataset and Oulu-CASIA Dataset
3.3 Comparison with State-of-the-art4 Conclusions; References; Robust Face Recognition Against Eyeglasses Interference by Integrating Local and Global Facial Features; 1 Introduction; 2 A Brief Review of Local Facial Feature Extraction Method; 2.1 Facial Feature Extraction Using the LGBPHS Method; 2.2 Facial Feature Extraction Using the Ununiformed Division Strategy; 3 The Proposed Feature Fusion Method; 3.1 Method Analysis; 3.2 The Procedure of the Proposed Method; 4 Experimental Evaluations; 5 Conclusion and Discussion; References
Face Recognition by Coarse-to-Fine Landmark Regression with Application to ATM Surveillance1 Introduction; 2 Related Work; 2.1 Face Landmark Localization; 2.2 Face Recognition; 3 Our Approach; 3.1 Pipeline; 3.2 Coarse-to-Fine Landmark Localization; 4 Face Recognition; 5 Experiments; 5.1 Landmark Localization; 5.2 Face Recognition Based on Landmark Locations; 6 Conclusion; References; An Efficient System for Partial Occluded Face Recognition; 1 Introduction; 2 The Challenges for a Robust Face Recognition System; 3 System Structure and the Sub-modules of the System
Summary
Annotation This three volume set, CCIS 771, 772, 773, constitutes the refereed proceedings of the CCF Chinese Conference on Computer Vision, CCCV 2017, held in Tianjin, China, in October 2017. The total of 174 revised full papers presented in three volumes were carefully reviewed and selected from 465 submissions. The papers are organized in the following topical sections: biological vision inspired visual method; biomedical image analysis; computer vision applications; deep neural network; face and posture analysis; image and video retrieval; image color and texture; image composition; image quality assessment and analysis; image restoration; image segmentation and classification; image-based modeling; object detection and classification; object identification; photography and video; robot vision; shape representation and matching; statistical methods and learning; video analysis and event recognition; visual salient detection
Notes
International conference proceedings
Includes author index
Online resource; title from PDF title page (SpringerLink, viewed December 11, 2017)
