On solving the face recognition problem with one training sample per subject

Authors:
Jie Wang;K. N. Plataniotis;Juwei Lu;A. N. Venetsanopoulos
Affiliations:
The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ont., Canada, M5A 3G4;The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ont., Canada, M5A 3G4;The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ont., Canada, M5A 3G4;The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ont., Canada, M5A 3G4
Venue:
Pattern Recognition
Year:
2006

Citing 32
Cited 16

Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners

IEEE Transactions on Pattern Analysis and Machine Intelligence
Original Contribution: Stacked generalization

Neural Networks
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
On Combining Classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem

Neural Computation
The FERET Evaluation Methodology for Face-Recognition Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class

IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network Ensembles

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: Features Versus Templates

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition with one training image per person

Pattern Recognition Letters
Multiclassifier Systems: Back to the Future

MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Component-based LDA Method for Face Recognition with One Training Sample

AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Face recognition: A literature survey

ACM Computing Surveys (CSUR)
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
The CMU Pose, Illumination, and Expression Database

IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhanced (PC)2 A for face recognition with one training image per person

Pattern Recognition Letters
A Unified Framework for Subspace Face Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Locally Linear Discriminant Analysis for Multimodally Distributed Classes for Face Recognition with a Single Model Image

IEEE Transactions on Pattern Analysis and Machine Intelligence
Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition

Pattern Recognition Letters
Generalized Discriminant Analysis Using a Kernel Approach

Neural Computation
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Classifier combination based on confidence transformation

Pattern Recognition
Efficient 3D reconstruction for face recognition

Pattern Recognition
Rapid and brief communication: An efficient kernel discriminant analysis method

Pattern Recognition
Selecting discriminant eigenfaces for face recognition

Pattern Recognition Letters
Resampling for face recognition

AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Designing multiple classifier systems for face recognition

MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Supervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Nonlinear kernel-based statistical pattern analysis

IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms

IEEE Transactions on Neural Networks
Face recognition using kernel direct discriminant analysis algorithms

IEEE Transactions on Neural Networks
Recognizing partially occluded, expression variant faces from single training image per person with SOM and soft k-NN ensemble

IEEE Transactions on Neural Networks

Uncorrelated multilinear principal component analysis through successive variance maximization

Proceedings of the 25th international conference on Machine learning
Face annotation for personal photos using context-assisted face recognition

MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Gaussian kernel optimization for pattern classification

Pattern Recognition
A novel kernel-based maximum a posteriori classification method

Neural Networks
Color face recognition for degraded face images

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Random translational transformation for changeable face verification

DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Uncorrelated multilinear principal component analysis for unsupervised multilinear subspace learning

IEEE Transactions on Neural Networks
Sorted index numbers for privacy preserving face recognition

EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective
Robust, accurate and efficient face recognition from a single training image: A uniform pursuit approach

Pattern Recognition
An analysis of random projection for changeable and privacy-preserving biometric verification

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A comparative study of preprocessing mismatch effects in color image based face recognition

Pattern Recognition
A study of online asynchronous learning monitored by face recognition

WSEAS Transactions on Information Science and Applications
Technical Section: Neural network-based symbol recognition using a few labeled samples

Computers and Graphics
Contribution of non-scrambled chroma information in privacy-protected face images to privacy leakage

IWDW'11 Proceedings of the 10th international conference on Digital-Forensics and Watermarking
Adaptive discriminant learning for face recognition

Pattern Recognition
Single sample face recognition based on DCT and local gabor binary pattern histogram

ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories

Quantified Score

Hi-index	0.01

Visualization

Abstract

The lack of adequate training samples and the considerable variations observed in the available image collections due to aging, illumination and pose variations are the two key technical barriers that appearance-based face recognition solutions have to overcome. It is a well-documented fact that their performance deteriorates rapidly when the number of training samples is smaller than the dimensionality of the image space. This is especially true for face recognition applications where only one training sample per subject is available. In this paper, a recognition framework based on the concept of the so-called generic learning is introduced as an attempt to boost the performance of traditional appearance-based recognition solutions in the one training sample application scenario. Different from contemporary approaches, the proposed solution learns the intrinsic properties of the subjects to be recognized using a generic training database which consists of images from subjects other than those under consideration. Many state-of-the-art face recognition solutions can be readily integrated in the proposed framework. A novel multi-learner framework is also proposed to further boost recognition performance. Extensive experimentation reported in the paper suggests that the proposed framework provides a comprehensive solution and achieves lower error recognition rate when considered in the context of one training sample face recognition problem.