207320252
An efficient kernel discriminant analysis method
Small sample size and high computational complexity are two major problems encountered when traditional kernel discriminant analysis methods are applied to high-dimensional pattern classification tasks such as face recognition. In this paper, we introduce a new kernel discriminant learning method, which is able to effectively address the two problems by using regularization and subspace decomposition techniques. Experiments performed on real face databases indicate that the proposed method outperforms, in terms of classification accuracy, existing kernel methods, such as kernel principal component analysis and kernel linear discriminant analysis, at a significantly reduced computational cost.
3104346
Selecting discriminant eigenfaces for face recognition
In realistic face recognition applications, such as surveillance photo identification, supervised learning algorithms usually fail when only one training sample per subject is available. The lack of training samples and the considerable image variations due to aging, illumination and pose variations, make recognition a challenging task. This letter proposes a development of the traditional eigenface solution by applying a feature selection process on the extracted eigenfaces. The proposal calls for the establishment of a feature subspace in which the intrasubject variation is minimized while the intersubject variation is maximized. Extensive experimentation following the FERET evaluation protocol suggests that in the scenario considered here, the proposed scheme improves significantly the recognition performance of the eigenface solution and outperforms other state-of-the-art methods.
12301513
A Comparative Study of Skin-Color Models
In this paper, we report the results of a comparative study on skin-color models generally used for facial region location. These include two 2D Gaussian models developed in normalized RGB and HSV color spaces respectively, a 1D lookup table model of hue histogram, and an adaptive 3D threshold box model. Also, we present a new model – called “adaptive hue lookup table”. The model is developed by introducing the so-called “Continuously Adaptive Mean Shift” (Camshift) technique into a traditional hue lookup table method. With the introduction of Camshift, the lookup table is able to adaptively adjust its parameters to fit the illumination conditions of different test images. In the experiments reported here, we compare the proposed method with the four typical skin-color filters in the scenarios of different human races and illuminations. The obtained results indicate that the proposed method reaches the best balance between false detection and detect rate.
5570863
Solving the small sample size problem in face recognition using generalized discriminant analysis
The goal of face recognition is to distinguish persons via their facial images. Each person's images form a cluster, and a new image is recognized by assigning it to the correct cluster. Since the images are very high-dimensional, it is necessary to reduce their dimension. Linear discriminant analysis (LDA) has been shown to be effective at dimension reduction while preserving the cluster structure of the data. It is classically defined as an optimization problem involving covariance matrices that represent the scatter within and between clusters. The requirement that one of these matrices be nonsingular restricts its application to datasets in which the dimension of the data does not exceed the sample size. For face recognition, however, the dimension typically exceeds the number of images in the database, resulting in what is referred to as the small sample size problem. Recently, the applicability of LDA has been extended by using the generalized singular value decomposition (GSVD) to circumvent the nonsingularity requirement, thus making LDA directly applicable to face recognition data. Our experiments confirm that LDA/GSVD solves the small sample size problem very effectively as compared with other current methods.
18193431
A framework of 2D Fisher discriminant analysis: application to face recognition with small number of training samples
A novel framework called 2D Fisher discriminant analysis (2D-FDA) is proposed to deal with the small sample size (SSS) problem in conventional one-dimensional linear discriminant analysis (1D-LDA). Different from the 1D-LDA based approaches, 2D-FDA is based on 2D image matrices rather than column vectors so the image matrix does not need to be transformed into a long vector before feature extraction. The advantage arising in this way is that the SSS problem does not exist any more because the between-class and within-class scatter matrices constructed in 2D-FDA are both of full-rank. This framework contains unilateral and bilateral 2D-FDA. It is applied to face recognition where only few training images exist for each subject. Both the unilateral and bilateral 2D-FDA achieve excellent performance on two public databases: ORL database and Yale face database B.
35219345
Integrated architectural modeling and analysis for high-assurance command and control system design
A real-time architectural specification (RAS) approach and its application to command and control (C2) systems are presented. The objective is to establish a formal foundation that will enable us to integrate existing rich but fragmented formal techniques for system specification and verification into practical and scaleable formal engineering methods to support the design and development of highly reliable real-time distributed systems. The contribution of RAS is twofold; first, it provides a formal system that integrates the system's timing requirements and the propagation of the requirements into the process of architectural modeling and design and provides a systematic way to enforce that the requirements are met in every step of the design process. Second, it offers an incremental and more scalable approach for design modeling. These two features together make RAS a suitable model for the design of C2 systems. Based on these features, we further present an incremental method for verifying timing properties of a RAS model that helps to reduce the complexity of analysis both at a given design level or across different design levels.
13574031
Mining genes in DNA using GeneScout
In this paper we present a new system, called GeneScout, for predicting gene structures in vertebrate genomic DNA. The system contains specially designed hidden Markov models (HMMs) for detecting functional sites including protein-translation start sites, mRNA splicing junction donor and acceptor sites, etc. Our main hypothesis is that, given a vertebrate genomic DNA sequence S, it is always possible to construct a directed acyclic graph G such that the path for the actual coding region of S is in the set of all paths on G. Thus, the gene detection problem is reduced to that of analyzing the paths in the graph G. A dynamic programming algorithm is used to find the optimal path in G. The proposed system is trained using an expectation-maximization (EM) algorithm and its performance on vertebrate gene prediction is evaluated using the 10-way cross-validation method. Experimental results show the good performance of the proposed system and its complementarity to a widely used gene detection system.
42707225
A Web-based Course in English as a Second Language: A Case Study