Active shape models with invariant optimal features (IOF-ASMs)

Authors:
Federico Sukno;Sebastián Ordás;Costantine Butakoff;Santiago Cruz;Alejandro Frangi
Affiliations:
Department of Technology, Pompeu Fabra University, Barcelona, Spain;Department of Technology, Pompeu Fabra University, Barcelona, Spain;Department of Technology, Pompeu Fabra University, Barcelona, Spain;Aragon Institute of Engineering Research, University of Zaragoza, Spain;Department of Technology, Pompeu Fabra University, Barcelona, Spain
Venue:
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Year:
2005

Citing 7
Cited 4

Active shape models—their training and application

Computer Vision and Image Understanding
Automatic Interpretation and Coding of Face Images Using Flexible Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
An optimal algorithm for approximate nearest neighbor searching fixed dimensions

Journal of the ACM (JACM)
Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications

Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications
Active Appearance Models

ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Error analysis of pattern recognition systems: the subsets bootstrap

Computer Vision and Image Understanding
Distance measures for PCA-based face recognition

Pattern Recognition Letters

Active Shape Models with Invariant Optimal Features: Application to Facial Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Projective active shape models for pose-variant image analysis of quasi-planar objects: Application to facial analysis

Pattern Recognition
Coarse-to-fine statistical shape model by Bayesian inference

ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Automatic pose correction for local feature-based face authentication

AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper is framed in the field of statistical face analysis. In particular, the problem of accurate segmentation of prominent features of the face in frontal view images is addressed. Our method constitutes an extension of Cootes et al. [6] linear Active Shape Model (ASM) approach, which has already been used in this task [9]. The technique is built upon the development of a non-linear appearance model, incorporating a reduced set of differential invariant features as local image descriptors. These features are invariant to rigid transformations, and a subset of them is chosen by Sequential Feature Selection (SFS) for each landmark and resolution level. The new approach overcomes the unimodality and gaussianity assumptions of classical ASMs regarding the distribution of the intensity values across the training set. Validation of the method is presented against the linear ASM and its predecesor, the Optimal Features ASM (OF-ASM) [14] using the AR and XM2VTS databases as testbed.