Model-based recognition in robot vision
ACM Computing Surveys (CSUR)
Active shape models—their training and application
Computer Vision and Image Understanding
A non-linear generalisation of PDMs using polynomial regression
BMVC 94 Proceedings of the conference on British machine vision (vol. 2)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems
Theoretical Computer Science
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Face Detection
International Journal of Computer Vision
Convex Optimization
Active Appearance Models Revisited
International Journal of Computer Vision
A Bayesian Mixture Model for Multi-View Face Alignment
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Active Shape Models with Invariant Optimal Features: Application to Facial Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Metamorphs: Deformable Shape and Appearance Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locating Facial Features with an Extended Active Shape Model
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of sparse point distribution models
Machine Vision and Applications
Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
Computer Vision: Algorithms and Applications
Computer Vision: Algorithms and Applications
| Hi-index | 0.00 |
It is well-known that, during shape extraction, enrolling an appropriate shape constraint model could effectively improve locating accuracy. In this paper, a novel deformable shape model, Sparse Representation Shape Models (SRSM), is introduced. Rather than following commonly utilized statistical shape constraints, our model constrains shape appearance based on a morphological structure, the convex hull of aligned training samples, i.e., only shapes that could be linearly represented by aligned training samples with the sum of coefficients equal to one, are defined as qualified. This restriction strictly controls shape deformation modes to reduce extraction errors and prevent extremely poor outputs. This model is realized based on sparse representation, which ensures during shape regularization the maximum valuable shape information could be reserved. Besides, SRSM is interpretable and hence helpful to further understanding applications, such as face pose recognition. The effectiveness of SRSM is verified on two publicly available face image datasets, the FGNET and the FERET.