5736636
Deformation Analysis for 3D Face Matching
Current two-dimensional image based face recognition systems encounter difficulties with large facial appearance variations due to the pose, illumination and expression changes. Utilizing 3D information of human faces is promising to handle the pose and lighting variations. While the 3D shape of a face does not change due to head pose (rigid) and lighting changes, it is not invariant to the non-rigid facial movement and evolution, such as expressions and aging effect. We propose a face surface matching framework to take into account both rigid and non-rigid variations to match a 2.5D face image to a 3D face model. The rigid registration is achieved by a modified Iterative Closest Point (ICP) algorithm. The thin plate spline (TPS) model is applied to estimate the deformation displacement vector field, which is used to represent the non-rigid deformation. For the purpose of face matching, the non-rigid deformations from different sources are identified, which is formulated as a two-class classification problem: intra-subject deformation vs. inter-subject deformation. The deformation classification results are integrated with the matching distances to make the final decision. Experimental results on a database containing 100 3D face models and 98 2.5D scans with smiling expression show that the number of errors is reduced from 28 to 18.
1730176
Integrating Range and Texture Information for 3D Face Recognition
The performance of face recognition systems that use two-dimensional images depends on consistent conditions w.r.t. lighting, pose, and facial appearance. We are developing a face recognition system that utilizes three-dimensional shape information to make the system more robust to arbitrary view, lighting, and facial appearance. For each subject, a 3D face model is constructed by integrating several 2.5D face scans from different viewpoints. A 2.5D scan is composed of one range image along with a registered 2D color image. The recognition engine consists of two components, surface matching and appearance-based matching. The surface matching component is based on a modified Iterative Closest Point (ICP) algorithm.The candidate list used for appearance matching is dynamically generated based on the output of the surface matching component, which reduces the complexity of the appearance-based matching stage. The 3D model in the gallery is used to synthesize new appearance samples with pose and illumination variations that are used for discriminant subspace analysis. The weighted sum rule is applied to combine the two matching components. A hierarchical matching structure is designed to further improve the system performance in both accuracy and efficiency. Experimental results are given for matching a database of 100 3D face models with 598 2.5D independent test scans acquired in different pose and lighting conditions, and with some smiling expression. The results show the feasibility of the proposed matching scheme.
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.
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.
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.
43527681
Learning-based pulmonary nodule detection from multislice CT data
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.
43125969
On claws belonging to every tournament
A directed graph is said to ben-unavoidable if it is contained as a subgraph by every tournament onn vertices. A number of theorems have been proven showing that certain graphs aren-unavoidable, the first being Rédei's results that every tournament has a Hamiltonian path. M. Saks and V. Sós gave more examples in [6] and also a conjecture that states: Every directed claw onn vertices such that the outdegree of the root is at most [n/2] isn-unavoidable. Here a claw is a rooted tree obtained by identifying the roots of a set of directed paths. We give a counterexample to this conjecture and prove the following result:any claw of rootdegree≤n/4 is n-unavoidable.
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.
23366254
Autonomous Information Services Integration for High-Assurance in Demand-Oriented Information Systems
With interdependent requests of multi-service increasing, the need for adaptability, timeliness and reliability to information service systems has become more and more important. In this paper, the relationship between the timeliness and the reliability of correlated services allocation and access is clarified. Based on these factors, the autonomous network-based information services integration technology to provide one-stop service for users’ multi-service requests is proposed. We proved the effectiveness of the proposed technology through the simulation and the results show that the integrated service can reduce the total users access time compared with the 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%.