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Small sample size is one of the most challenging problems in face recognition due to the difficulty of sample collection in many real-world applications. By representing the query sample as a linear combination of training samples from all classes, the so-called collab-orative representation based classification (CRC) shows very effective face recognition(More)
In this paper we propose a two-dimensional (2D) Laplacianfaces method for face recognition. The new algorithm is developed based on two techniques, i.e., locality preserved embedding and image based projection. The 2D Laplacianfaces method is not only computationally more efficient but also more accurate than the one-dimensional (1D) Laplacianfaces method(More)
By representing the input testing image as a sparse linear combination of the training samples via l 1-norm minimization, sparse representation based classification (SRC) has shown promising results for face recognition (FR). Particularly, by introducing an identity occlusion dictionary to code the occluded portions of face images, SRC could lead to robust(More)
With the rapid development of digital imaging and communication technologies, image set based face recognition (ISFR) is becoming increasingly important. One key issue of ISFR is how to effectively and efficiently represent the query face image set by using the gallery face image sets. The set-to-set distance based methods ignore the relationship between(More)
—CBR systems that are built for the classification problems are called CBR classifiers. This paper presents a novel and fast approach to building efficient and competent CBR classifiers that combines both feature reduction (FR) and case selection (CS). It has three central contributions: 1) it develops a fast rough-set method based on relative attribute(More)
This paper presents a novel rough set-based case-based reasoner for use in text categorization (TC). The reasoner has four main components: feature term extractor, document representor, case selector, and case retriever. It operates by first reducing the number of feature terms in the documents using the rough set technique. Then, the number of documents is(More)
Factors such as misalignment, pose variation, and occlusion make robust face recognition a difficult problem. It is known that statistical features such as local binary pattern are effective for local feature extraction, whereas the recently proposed sparse or collaborative representation-based classification has shown interesting results in robust face(More)
This paper proposes a methodology of maintaining Case Based Reasoning (CBR) systems by using fuzzy decision tree induction-a machine learning technique. The methodology is mainly based on the idea that a large case library can be transformed to a small case library together with a group of adaptation rules, which are generated by fuzzy decision trees.(More)