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.
7724153
Resampling for Face Recognition
A number of applications require robust human face recognition under varying environmental lighting conditions and different facial expressions, which considerably vary the appearance of human face. However, in many face recognition applications, only a small number of training samples for each subject are available; these samples are not able to capture all the facial appearance variations. We utilize the resampling techniques to generate several subsets of samples from the original training dataset. A classic appearance-based recognizer, LDA-based classifier, is applied to each of the generated subsets to construct a LDA representation for face recognition. The classification results from each subset are integrated by two strategies: majority voting and the sum rule. Experiments conducted on a face database containing 206 subjects (2,060 face images) show that the proposed approaches improve the recognition accuracy of the classical LDA-based face classifier by about 7 percentages.
5510684
Robust motion-based image segmentation using fusion
To support real-time tracking of objects in video sequences, there has been considerable effort directed at developing optical flow and general motion-based image segmentation algorithms. The goal is to segment multiple moving objects in the image based on their relative motion. This task can be complicated by the presence of lighting variations. Furthermore, a combination of multiple motions and complex lighting effects can lead to dramatic image variations that may not be adequately accounted for by a single motion-based segmentation algorithm. We propose to fuse the results of multiple motion segmentation algorithms to improve the system robustness. Our approach uses the expectation maximization (EM) algorithm as a fusion engine. It also uses principal components analysis (PCA) to perform dimensionality reduction to improve the performance of the EM algorithm and reduce the processing burden. The performance of the proposed fusion algorithm has been demonstrated in the "smart airbag" application of monitoring occupants in a moving automobile to determine if they are too close to the instrument panel (airbag). Through fusion of the outputs of multiple algorithms we are able to reduce the percentage of pixels missed on the target by 35.
11312001
Face Recognition with 3D Model-Based Synthesis
Current appearance-based face recognition system encounters the difficulty to recognize faces with appearance variations, while only a small number of training images are available. We present a scheme based on the analysis by synthesis framework. A 3D generic face model is aligned onto a given frontal face image. A number of synthetic face images are generated with appearance variations from the aligned 3D face model. These synthesized images are used to construct an affine subspace for each subject. Training and test images for each subject are represented in the same way in such a subspace. Face recognition is achieved by minimizing the distance between the subspace of a test subject and that of each subject in the database. Only a single face image of each subject is available for training in our experiments. Preliminary experimental results are promising.
176930
Face detection and synthesis using Markov random field models
Markov random fields (MRFs) are proposed as viable stochastic models for the spatial distribution of gray levels for images of human faces. These models are trained using data bases of face and non-face images. The trained MRF models are then used for detecting human faces in test images. We investigate the performance of the face detection algorithm for two classes of MRFs given by the first- and second-order neighborhood systems. From the cross validation results and from actual detection in real images, it is shown that the second-order model makes fewer false detections. We also investigate the possibility of increasing our training data base of faces by simulating face-like images from the trained MRFs. The performance of the re-trained MRFs based on added face-like images is compared to the original training data base.
41811385
Orientation analysis for rotated human face detection
Abstract In this paper, we present a framework of orientation analysis for rotated human face detection. A novel orientation histogram is constructed for statistical analysis of face images, which can show a proper symmetry with respect to the orientation of the principal axis and a local peak at its orthogonal orientation. By analyzing the orientation histogram, we can easily find the principal axis' orientation of the rotated face. Then an upright frontal face detector based on orientation analysis and other knowledge is designed to verify the given face. The experimental results illustrate the effectiveness of our framework.
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.
16545185
Hybrid Methods for POS Guessing of Chinese Unknown Words
This paper describes a hybrid model that combines a rule-based model with two statistical models for the task of POS guessing of Chinese unknown words. The rule-based model is sensitive to the type, length, and internal structure of unknown words, and the two statistical models utilize contextual information and the likelihood for a character to appear in a particular position of words of a particular length and POS category. By combining models that use different sources of information, the hybrid model achieves a precision of 89%, a significant improvement over the best result reported in previous studies, which was 69%.
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.
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
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.