Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Integrating Faces and Fingerprints for Personal Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Palmprint Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Communications of the ACM - Multimodal interfaces that flex, adapt, and persist
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature-Level Fusion in Personal Identification
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Biometric Identification System Based on Eigenpalm and Eigenfinger Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Continuous Verification Using Multimodal Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Score normalization in multimodal biometric systems
Pattern Recognition
Exploiting global and local decisions for multimodal biometrics verification
IEEE Transactions on Signal Processing - Part II
Biometrics: a tool for information security
IEEE Transactions on Information Forensics and Security
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Fast communication: Gait recognition based on dynamic region analysis
Signal Processing
Threshold-optimized decision-level fusion and its application to biometrics
Pattern Recognition
Reduced-reference IQA in contourlet domain
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Discriminative orthogonal neighborhood-preserving projections for classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Writer identification using fractal dimension of wavelet subbands in gabor domain
Integrated Computer-Aided Engineering
Pattern Recognition Letters
A cascade fusion scheme for gait and cumulative foot pressure image recognition
Pattern Recognition
Fusion of biometric systems using Boolean combination: an application to iris-based authentication
International Journal of Biometrics
Journal of Visual Communication and Image Representation
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Multimodal biometric system utilizes two or more individual modalities, e.g., face, gait, and fingerprint, to improve the recognition accuracy of conventional unimodal methods. However, existing multimodal biometric methods neglect interactions of different modalities during the subspace selection procedure, i.e., the underlying assumption is the independence of different modalities. In this paper, by breaking this assumption, we propose a Geometry Preserving Projections (GPP) approach for subspace selection, which is capable of discriminating different classes and preserving the intra-modal geometry of samples within an identical class. With GPP, we can project all raw biometric data from different identities and modalities onto a unified subspace, on which classification can be performed. Furthermore, the training stage is carried out once and we have a unified transformation matrix to project different modalities. Unlike existing multimodal biometric systems, the new system works well when some modalities are not available. Experimental results demonstrate the effectiveness of the proposed GPP for individual recognition tasks.