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Ensemble-based discriminant learning with boosting for face recognition
- 10, 2009 DRAFT IVP/713183.V2 23
, 2006
"... 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 beli ..."
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Cited by 32 (6 self)
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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.
Face recognition via adaptive discriminant clustering
- IEEE International Conference on Image Processing
, 2008
"... This paper presents a methodology that tackles the face recognition problem by accommodating multiple clustering steps. At each clustering step, the test and training faces are projected to a discriminant space and the projected training data are partitioned into clusters using the k-means algorithm ..."
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Cited by 2 (1 self)
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This paper presents a methodology that tackles the face recognition problem by accommodating multiple clustering steps. At each clustering step, the test and training faces are projected to a discriminant space and the projected training data are partitioned into clusters using the k-means algorithm. Then a subset of the training data clusters is selected, based on how similar the faces in these clusters are to the test face. In the clustering step that follows a new discriminant space is defined by processing this subset and both the test and training data are projected to this space. This process is repeated until one final cluster is selected and the most similar, to the test face, face class contained is set as the identity match. The UMIST and XM2VTS face databases have been used to evaluate the algorithm and results indicate that the proposed framework provides a promising solution to the face recognition problem. Index Terms — adaptive classification, face recognition, discriminant analysisi 1.
UNSUPERVISED DISCOVERY OF VISUAL FACE CATEGORIES
, 2012
"... Human faces can be arranged into different face categories using information from common visual cues such as gender, ethnicity, and age. It has been demonstrated that using face categorization as a precursor step to face recognition improves recognition rates and leads to more graceful errors. 1 Alt ..."
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Cited by 1 (1 self)
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Human faces can be arranged into different face categories using information from common visual cues such as gender, ethnicity, and age. It has been demonstrated that using face categorization as a precursor step to face recognition improves recognition rates and leads to more graceful errors. 1 Although face categorization using common visual cues yields meaningful face categories, developing accurate and robust gender, ethnicity, and age categorizers is a challenging issue. Moreover, it limits the overall number of possible face categories and, in practice, yields unbalanced face categories which can compromise recognition performance. This paper investigates ways to automatically discover a categorization of human faces from a collection of unlabeled face images without relying on predefined visual cues. Specifically, given a set of face images from a group of known individuals (i.e., gallery set), our goal is finding ways to robustly partition the gallery set (i.e., face categories). The objective is being able to assign novel images of the same individuals (i.e., query set) to the correct face category with high accuracy and robustness. To address the issue of face category discovery, we represent faces using local features and apply unsupervised learning (i.e., clustering). To categorize faces in novel images, we employ nearest-neighbor algorithms 1250029-1 S. Yang et al.
Entropy-based Iterative Face Classification
"... Abstract. This paper presents a novel methodology whose task is to deal with the face classification problem. This algorithm uses discriminant analysis to project the face classes and a clustering algorithm to partition the projected face data, thus forming a set of discriminant clusters. Then, an ..."
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Abstract. This paper presents a novel methodology whose task is to deal with the face classification problem. This algorithm uses discriminant analysis to project the face classes and a clustering algorithm to partition the projected face data, thus forming a set of discriminant clusters. Then, an iterative process creates subsets, whose cardinality is defined by an entropybased measure, that contain the most useful clusters. The best match to the test face is found when one final face class is retained. The standard UMIST and XM2VTS databases have been utilized to evaluate the performance of the proposed algorithm. Results show that it provides a good solution to the face classification problem.
Bayesian Face Recognition Based on Gaussian Mixture Models
"... Bayesian analysis is a popular subspace based face recognition method. It casts the face recognition task into a binary classification problem with each of the two classes, intrapersonal variation and extrapersonal variation, modeled as a Gaussian distribution. However, with the existence of signifi ..."
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Bayesian analysis is a popular subspace based face recognition method. It casts the face recognition task into a binary classification problem with each of the two classes, intrapersonal variation and extrapersonal variation, modeled as a Gaussian distribution. However, with the existence of significant transformations, such as large illumination and pose changes, the intrapersonal facial variation cannot be modeled as a single Gaussian distribution, and the global linear subspace often fails to deliver good performance on the complex non-convex data set. In this paper, we extend the Bayesian face recognition into Gaussian mixture models. The complex intrapersonal variation manifold is learnt by a set of local linear intrapersonal subspaces and thus can be effectively reduced. The effectiveness of the novel method is demonstrated by experiments on the data set from AR face database containing 2340 face images. 1.
