Ensemble-based discriminant learning with boosting for face recognition

Abstract

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.Index Terms-Boosting, face recognition (FR), linear discriminant analysis, machine learning, mixture of linear models, smallsample-size (SSS) problem, strong learner.

Fully corrective boosting with arbitrary loss and regularization

“…We set conventional confidence level to be 95% and the rejection region is {w ∈ R | w > 70}, considering the number of datasets with different performance is 13. 7 The output of WSRT is illustrated in Table 4.…”

“…Much effort has been devoted to analyzing AdaBoost [2] and other boosting algorithms [3,4,5] due to their great success in both classification and regression when applied to a wide variety of computer vision and machine learning tasks (see [7,8] for example). Both theoretical and experimental results have shown that boosting algorithms have impressive generalization performance.…”

A Survey of Face Recognition Techniques

“…Some other AI approaches utilized for the face recognition task include evolutionary pursuit [181,182] and techniques [183,184] based on boosting [161,185]. These schemes have reportedly yielded promising results for various difficult face recognition scenarios.…”

A Comparative Study of Baseline Algorithms of Face Recognition

“…The pseudo code of AdaBoost-LDA based face recognition system is given in Table I. Further details of this recognition system can be found in [19], [23].…”

“…In PCA based face recognition system, first of all face images are decomposed into small sets of featured images, that are actually the Principal Components or Eigenfaces of initial training set [19]. Then, all centred images are projected into face space by multiplying in Eigenface basis's.…”