Ensemble-based discriminant learning with boosting for face recognition.

The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, ON M5S 3G4, Canada.
IEEE Transactions on Neural Networks (Impact Factor: 2.95). 02/2006; 17(1):166-78. DOI: 10.1109/TNN.2005.860853
Source: IEEE Xplore

ABSTRACT In this paper, we propose a novel ensemble-based approach to boost performance of traditional Linear Discriminant Analysis (LDA)-based methods used in face recognition. The ensemble-based approach is based on the recently emerged technique known as "boosting". However, it is generally believed that boosting-like learning rules are not suited to a strong and stable learner such as LDA. To break the limitation, a novel weakness analysis theory is developed here. The theory attempts to boost a strong learner by increasing the diversity between the classifiers created by the learner, at the expense of decreasing their margins, so as to achieve a tradeoff suggested by recent boosting studies for a low generalization error. In addition, a novel distribution accounting for the pairwise class discriminant information is introduced for effective interaction between the booster and the LDA-based learner. The integration of all these methodologies proposed here leads to the novel ensemble-based discriminant learning approach, capable of taking advantage of both the boosting and LDA techniques. Promising experimental results obtained on various difficult face recognition scenarios demonstrate the effectiveness of the proposed approach. We believe that this work is especially beneficial in extending the boosting framework to accommodate general (strong/weak) learners.

1 Bookmark
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper proposes a novel method of supervised and unsupervised multi-linear neighborhood preserving projection (MNPP) for face recognition. Unlike conventional neighborhood preserving projections, the MNPP method operates directly on tensorial data rather than vectors or matrices, and solves problems of tensorial representation for multi-dimensional feature extraction, classification and recognition. As opposed to traditional approaches such as NPP and 2DNPP, which derive only one subspace, multiple interrelated subspaces are obtained in the MNPP method by unfolding the tensor over different tensorial directions. The number of subspaces derived by MNPP is determined by the order of the tensor space. This approach is used for face recognition and biometrical security classification problems involving higher order tensors. The performance of our proposed and existing techniques is analyzed using three benchmark facial datasets ORL, AR, and FERET. The obtained results show that the MNPP outperforms the standard approaches in terms of the error rate.
    Pattern Recognition. 02/2014; 47(2):544-555.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Two main issues for event-related potential (ERP) classification in brain-computer interface (BCI) application are curse-of-dimensionality and bias-variance tradeoff, which may deteriorate classification performance, especially with insufficient training samples resulted from limited calibration time. This study introduces an aggregation of sparse linear discriminant analyses (ASLDA) to overcome these problems. In the ASLDA, multiple sparse discriminant vectors are learned from differently l1-regularized least-squares regressions by exploiting the equivalence between LDA and least-squares regression, and are subsequently aggregated to form an ensemble classifier, which could not only implement automatic feature selection for dimensionality reduction to alleviate curse-of-dimensionality, but also decrease the variance to improve generalization capacity for new test samples. Extensive investigation and comparison are carried out among the ASLDA, the ordinary LDA and other competing ERP classification algorithms, based on different three ERP datasets. Experimental results indicate that the ASLDA yields better overall performance for single-trial ERP classification when insufficient training samples are available. This suggests the proposed ASLDA is promising for ERP classification in small sample size scenario to improve the practicability of BCI.
    International Journal of Neural Systems 02/2014; 24(1):1450003. · 5.05 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: This article assesses the feasibility of using shape information to detect and quantify the subcortical and ventricular structural changes in mild cognitive impairment (MCI) and Alzheimer's disease (AD) patients. We first demonstrate structural shape abnormalities in MCI and AD as compared with healthy controls (HC). Exploring the development to AD, we then divide the MCI participants into two subgroups based on longitudinal clinical information: (1) MCI patients who remained stable; (2) MCI patients who converted to AD over time. We focus on seven structures (amygdala, hippocampus, thalamus, caudate, putamen, globus pallidus, and lateral ventricles) in 754 MR scans (210 HC, 369 MCI of which 151 converted to AD over time, and 175 AD). The hippocampus and amygdala were further subsegmented based on high field 0.8 mm isotropic 7.0T scans for finer exploration. For MCI and AD, prominent ventricular expansions were detected and we found that these patients had strongest hippocampal atrophy occurring at CA1 and strongest amygdala atrophy at the basolateral complex. Mild atrophy in basal ganglia structures was also detected in MCI and AD. Stronger atrophy in the amygdala and hippocampus, and greater expansion in ventricles was observed in MCI converters, relative to those MCI who remained stable. Furthermore, we performed principal component analysis on a linear shape space of each structure. A subsequent linear discriminant analysis on the principal component values of hippocampus, amygdala, and ventricle leads to correct classification of 88% HC subjects and 86% AD subjects. Hum Brain Mapp, 2014. © 2014 Wiley Periodicals, Inc.
    Human Brain Mapping 01/2014; · 6.88 Impact Factor

Full-text (3 Sources)

Available from
May 29, 2014