4633793
Independent component analysis in a local facial residue space for face recognition
In this paper, we propose an Independent Component Analysis (ICA) based face recognition algorithm, which is robust to illumination andpose variation. Generally, it is well known that the 5rst few eigenfaces represent illumination variation rather than identity. Most Principal Component Analysis (PCA) based methods have overcome illumination variation by discarding the projection to a few leading eigenfaces. The space spanned after removing a few leading eigenfaces is called the “residual face space”. We found that ICA in the residual face space provides more e9cient encoding in terms of redundancy reduction androbustness to pose variation as well as illumination variation, owing to its ability to represent non-Gaussian statistics. Moreover, a face image is separatedinto several facial components, local spaces, andeach local space is representedby the ICA bases (independent components) of its corresponding residual space. The statistical models of face images in local spaces are relatively simple andfacilitate classi5cation by a linear encoding. Various experimental results show that the accuracy of face recognition is signi5cantly improvedby the proposedmethodunder large illumination andpose variations.
4644097
Component-based LDA face description for image retrieval and MPEG-7 standardisation
We propose a method of face description for facial image retrieval from a large data base and for MPEG-7 (Moving Picture Experts Group) standardisation. The novel descriptor is obtained by decomposing a face image into several components and then combining the component features. The decomposition combined with LDA (Linear Discriminant Analysis) provides discriminative facial descriptions that are less sensitive to light and pose changes. Each facial component is represented in its Fisher space and another LDA is then applied to compactly combine the features of the components. To enhance retrieval accuracy further, a simple pose classification and transformation technique is performed, followed by recursive matching. Our algorithm has been developed to deal with the problem of face image retrieval from huge databases such as those found in Internet environments. Such retrieval requires a compact face representation which has robust recognition performance under lighting and pose variations. The partitioning of a face image into components offers a number of benefits that facilitate the development of an efficient and robust face retrieval algorithm. Variation in image statistics due to pose and/or illumination changes within each component region can be simplified and more easily captured by a linear encoding than that of the whole image. So an LDA encoding at the component level facilitates better classification. Furthermore, a facial component can be weighted according to its importance. The component with a large variation is weighted less in the matching stage to yield a more reliable decision. The experimental results obtained on the MPEG-7 data set show an impressive accuracy of our algorithm as compared with other methods including conventional PCA (Principal Component Analysis)/ICA (Independent Component Analysis)/LDA methods and the previous MPEG-7 proposals.
9123990
Dimension Reduction in Text Classification with Support Vector Machines
Support vector machines (SVMs) have been recognized as one of the most successful classification methods for many applications including text classification. Even though the learning ability and computational complexity of training in support vector machines may be independent of the dimension of the feature space, reducing computational complexity is an essential issue to efficiently handle a large number of terms in practical applications of text classification. In this paper, we adopt novel dimension reduction methods to reduce the dimension of the document vectors dramatically. We also introduce decision functions for the centroid-based classification algorithm and support vector classifiers to handle the classification problem where a document may belong to multiple classes. Our substantial experimental results show that with several dimension reduction methods that are designed particularly for clustered data, higher efficiency for both training and testing can be achieved without sacrificing prediction accuracy of text classification even when the dimension of the input space is significantly reduced.
36946020
Face Representation Method Using Pixel-to-Vertex Map (PVM) for 3D Model Based Face Recognition
3D model based approach for face recognition has been spotlighted as a robust solution under variant conditions of pose and illumination. Since a generative 3D face model consists of a large number of vertices, a 3D model based face recognition system is generally inefficient in computation time. In this paper, we propose a novel 3D face representation algorithm to reduce the number of vertices and optimize its computation time. Finally, we evaluate the performance of proposed algorithm with the Korean face database collected using a stereo-camera based 3D face capturing device.
6849879
An ontology of quality for enterprise modelling
The TOVE Quality Ontology is the formal representation (using first-order logic) of terms, relationships, and axioms about quality which are generic beyond any specific quality domain. The assumption that quality is "conformance to requirements" is used to decompose the domain of quality into sub-domains of measurement, quality analysis, identification, and traceability. An ontological engineering methodology of posing ontological scope, stating competency questions, constructing data models and axioms, and visualization of the answering of competency questions is demonstrated with an example from the engineering of the traceability ontology.
38600553
WIPI Mobile Platform with Secure Service for Mobile RFID Network Environment
Recently, RFID (Radio Frequency Identification) technology is practically applied to a number of logistics processes as well as asset management, and RFID is also expected to be permeated in our daily life with the name of ’Ubiquitous Computing’ or ‘Ubiquitous Network’ within the near future. The R&D groups in global now have paid attention to integrate RFID with mobile devices as well as to associate with the existing mobile telecommunication network. Such a converged technology and services would lead to make new markets and research challenges. However, the privacy violation on tagged products has become stumbling block. We propose light-weight security mechanism which is constructed by mobile RFID security mechanism based on WIPI (Wireless Internet Platform for Interoperability). WIPI-based light-weight mobile RFID security platform can be applicable to various mobile RFID services that required secure business applications in mobile environment.
6231139
Practical and Provably-Secure Multicasting over High-Delay Networks
This paper considers the problem of authenticated key exchange in a dynamic group in which members join and leave the group in an arbitrary fashion. A group key exchange scheme for such a dynamic group is designed to minimize the cost of the rekeying operations associated with group updates. Although a number of schemes have attempted for many years to address this problem, all provably-secure schemes are inadequate in dealing with a dynamic group where group members are spread across a wide area network; their communication overhead for group rekeying is significant in terms of the number of communication rounds or the number of messages, both of which are recognized as the dominant factors that severely slow down group key exchange over a wide area network. In this paper, we propose an efficient key exchange scheme for this scenario and prove its security against an active adversary under the factoring assumption. The proposed scheme requires only a constant number of rounds while achieving low message complexity.