121253028
Three-dimensional model based face recognition
The performance of face recognition systems that use two-dimensional (2D) images is dependent on consistent conditions such as lighting, pose and facial expression. We are developing a multi-view face recognition system that utilizes three-dimensional (3D) information about the face to make the system more robust to these variations. This work describes a procedure for constructing a database of 3D face models and matching this database to 2.5D face scans which are captured from different views, using coordinate system invariant properties of the facial surface. 2.5D is a simplified 3D (x, y, z) surface representation that contains at most one depth value (z direction) for every point in the (x, y) plane. A robust similarity metric is defined for matching, based on an iterative closest point (ICP) registration process. Results are given for matching a database of 18 3D face models with 113 2.5D face scans.
43527681
Learning-based pulmonary nodule detection from multislice CT data
14450433
Integrating Faces, Fingerprints, and Soft Biometric Traits for User Recognition
Soft biometric traits like gender, age, height, weight, ethnicity, and eye color cannot provide reliable user recognition because they are not distinctive and permanent. However, such ancillary information can complement the identity information provided by the primary biometric traits (face, fingerprint, hand-geometry, iris, etc.). This paper describes a hybrid biometric system that uses face and fingerprint as the primary characteristics and gender, ethnicity, and height as the soft characteristics. We have studied the effect of the soft biometric traits on the recognition performance of unimodal face and fingerprint recognition systems and a multimodal system that uses both the primary traits. Experiments conducted on a database of 263 users show that the recognition performance of the primary biometric system can be improved significantly by making use of soft biometric information. The results also indicate that such a performance improvement can be achieved only if the soft biometric traits are complementary to the primary biometric traits.
18993736
Matching 2.5D Scans for Face Recognition
The performance of face recognition systems that use two-dimensional images is dependent on consistent conditions such as lighting, pose, and facial appearance. We are developing a face recognition system that uses three-dimensional depth information to make the system more robust to these arbitrary conditions. We have developed a face matching system that automatically correlates points in three dimensions between two 2.5D range images of different views. A hybrid Iterative Closest Point (ICP) scheme is proposed to integrate two classical ICP algorithms for fine registration of the two scans. A robust similarity metric is defined for matching purpose. Results are provided on a preliminary database of 10 subjects (one training image per subject) containing frontal face images of neutral expression with a testing database of 63 scans that varied in pose, expression and lighting.
19393995
Skilled Forgery Detection in On-Line Signatures: A Multimodal Approach
Signature recognition has a long history of usage in authentication of transactions and legal contracts and hence is easily accepted by users in a variety of applications. However, the problem of skilled forgeries is an important challenge that needs to be overcome before signature recognition systems will become viable in unsupervised authentication systems. In this paper, we present a multimodal approach to forgery detection, where a physiological trait, the face of the signing person, is used to validate the signature. Methods of normalizing and combining the matching scores from the individual modalities are investigated. Test results of the system on a database of 100 users is presented. The system achieves an equal error rate of 2.2% in the presence of high quality skilled forgeries and could detect all the skilled forgeries at a genuine acceptance rate of 75%.
59108819
Agent-Based Rating Oriented Information Provision and Reallocation for High-Assurance in Open and Dynamic Environments
In order to meet the heterogeneous requirementsfrom service providers and users simultaneously, thereis an urgent need for a new information service system.After studying the characteristics of the users' accessing,that popularity of information utilization generallyexists in information system is focused. Thispaper introduces a rating oriented distributed informationsystem sustained by push/pull mobile agents for informationservice provision and utilization. Based onthis environment, autonomous information allocationtechnology is proposed. However, when users' demandchanges, congestion might arise in the system. In orderto achieve load balance for users' timeliness accessand guarantee that users can always get the informationfrom a certain pathway, autonomous informationreallocation technology is proposed. Through the comparisonsimulation to the conventional system, the effectivenessof proposed system is proved.
14843520
Deriving knowledge from figures for digital libraries
Figures in digital documents contain important information. Current digital libraries do not summarize and index information available within figures for document retrieval. We present our system on automatic categorization of figures and extraction of data from 2-D plots. A machine-learning based method is used to categorize figures into a set of predefined types based on image features. An automated algorithm is designed to extract data values from solid line curves in 2-D plots. The semantic type of figures and extracted data values from 2-D plots can be integrated with textual information within documents to provide more effective document retrieval services for digital library users. Experimental evaluation has demonstrated that our system can produce results suitable for real-world use.
550060
Automatic categorization of figures in scientific documents
Figures are very important non-textual information contained in scientific documents. Current digital libraries do not provide users tools to retrieve documents based on the information available within the figures. We propose architecture for retrieving documents by integrating figures and other information. The initial step in enabling integrated document search is to categorize figures into a set of pre-defined types. We propose several categories of figures based on their functionalities in scholarly articles. We have developed a machine-learning-based approach for automatic categorization of figures. Both global features, such as texture, and part features, such as lines, are utilized in the architecture for discriminating among figure categories. The proposed approach has been evaluated on a testbed document set collected from the CiteSeer scientific literature digital library. Experimental evaluation has demonstrated that our algorithms can produce acceptable results for real- world use. Our tools can be integrated into a scientific-document digital library
20901797
Decentralized workload management for assurance according to heterogeneous service levels
Due to the advent of electronic commerce, the service provider can offer the customer a Service Level Agreement (SLA) to distinguish itself in today's competitive market. Different users require different levels of service in terms of information consumption or response time, appearing a need of a system able to assure the heterogeneous service levels of the users in such a mission-critical environment. In this paper, we propose an information service infrastructure based on the Faded Information Field (FIF), a distributed service replica network founded on push/pull mobile agent technology. The cooperation between the autonomous agents and the nodes allows the online maintenance and auto-reconstruction of the FIF system in order to satisfy the different timely requirements of each user even in an ever-changing environment. Moreover, the system is able to provide different maximum information volume according to the user's service level, a double-sided feature that benefits to both the provider's business strategy and the differentiated timeliness access technology presented on this paper. The effectiveness of the proposed technology has been proved through evaluation and the simulation results show that the system is able to adapt and satisfy the different requirements of timeliness and the different levels of information volume required by each user under changing workloads, compared with conventional systems.
12221705
Fast mode decision and motion estimation for H.264 with a focus on MPEG-2/H.264 transcoding
The H.264 standard achieves much higher coding efficiency than previous video coding standards, due to its improved inter and intra prediction modes which come with a cost of higher computational complexity. When transcoding to H.264 from MPEG-2, motion information from MPEG-2 can be used to speed up the motion search. Fast mode decision algorithms are proposed for B and P frames, and a fast intra prediction algorithm is developed for intra coding. In addition, fast motion estimation is developed by reusing the motion information from MPEG-2. Simulation results demonstrate that we can achieve significant complexity reduction while maintaining the coding efficiency.