Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
Using Uncorrelated Discriminant Analysis for Tissue Classification with Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Symmetrical null space LDA for face and ear recognition
Neurocomputing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
A unified framework for semi-supervised dimensionality reduction
Pattern Recognition
Fusion of color spaces for ear authentication
Pattern Recognition
Locality sensitive discriminant analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Uncorrelated discriminant simplex analysis for view-invariant gait signal computing
Pattern Recognition Letters
Toward unconstrained ear recognition from two-dimensional images
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
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In this paper, we propose an improved manifold learning method, called uncorrelated local Fisher discriminant analysis (ULFDA), for ear recognition. Motivated by the fact that the features extracted by local Fisher discriminant analysis are statistically correlated, which may result in poor performance for recognition. The aim of ULFDA is to seek a feature submanifold such that the within-manifold scatter is minimized and between-manifold scatter is maximized simultaneously in the embedding space by using a new difference-based optimization objective function. Moreover, we impose an appropriate constraint to make the extracted features statistically uncorrelated. As a result, the proposed algorithm not only derives the optimal and lossless discriminative information, but also guarantees that all extracted features are statistically uncorrelated. Experiments on synthetic data and Spain, USTB-2 and CEID ear databases are performed to demonstrate the effectiveness of the proposed method.