3D Face Recognition Benchmarks on the Bosphorus Database with Focus on Facial Expressions
Biometrics and Identity Management
Incremental construction of classifier and discriminant ensembles
Information Sciences: an International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
Palmprint recognition using 3-D information
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Discriminant nonnegative tensor factorization algorithms
IEEE Transactions on Neural Networks
3D face recognition with sparse spherical representations
Pattern Recognition
Subspace methods for retrieval of general 3D models
Computer Vision and Image Understanding
Regional registration for expression resistant 3-D face recognition
IEEE Transactions on Information Forensics and Security
Automatic face segmentation and facial landmark detection in range images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hybrid associative retrieval of three-dimensional models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Face recognition in 2D and 2.5D using ridgelets and photometric stereo
Pattern Recognition
Robust learning from normals for 3d face recognition
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Feature line extraction from unorganized noisy point clouds using truncated Fourier series
The Visual Computer: International Journal of Computer Graphics
| Hi-index | 0.00 |
In this paper, we present an extensive study of 3D face recognition algorithms and examine the benefits of various score-, rank-, and decision-level fusion rules. We investigate face recognizers from two perspectives: the data representation techniques used and the feature extraction algorithms that match best each representation type. We also consider novel applications of various feature extraction techniques such as discrete Fourier transform, discrete cosine transform, nonnegative matrix factorization, and principal curvature directions to the shape modality. We discuss and compare various classifier combination methods such as fixed rules and voting- and rank-based fusion schemes. We also present a dynamic confidence estimation algorithm to boost fusion performance. In identification experiments performed on FRGC v1.0 and FRGC v2.0 face databases, we have tried to find the answers to the following questions: 1) the relative importance of the face representation techniques vis-a-vis the types of features extracted; 2) the impact of the gallery size; 3) the conditions, under which subspace methods are preferable, and the compression factor; 4) the most advantageous fusion level and fusion methods; 5) the role of confidence votes in improving fusion and the style of selecting experts in the fusion; and 6) the consistency of the conclusions across different databases.