Bayesian Face Recognition Based on Markov Random Field Modeling
In this paper, a Bayesian method for face recognition is proposed based on Markov Random Fields (MRF) modeling. Constraints on image features as well as contextual relationships between them are explored and encoded into a cost function derived based on a statistical model of MRF. Gabor wavelet coefficients are used as the base features, and relationships between Gabor features at different pixel locations are used to provide higher order contextual constraints. The posterior probability of matching configuration is derived based on MRF modeling. Local search and discriminate analysis are used to evaluate local matches, and a contextual constraint is applied to evaluate mutual matches between local matches. The proposed MRF method provides a new perspective for modeling the face recognition problem. Experiments demonstrate promising results.

title: An Analysis-by-Synthesis Method for Heterogeneous Face Biometrics
abstract: Face images captured in different spectral bands, e.g. , in visual (VIS) and near infrared (NIR), are said to be heterogeneous. Although a person's face looks different in heterogeneous images, it should be classified as being from the same individual. In this paper, we present a new method, called face analogy , in the analysis-by-synthesis framework, for heterogeneous face mapping, that is, transforming face images from one type to another, and thereby performing heterogeneous face matching. Experiments show promising results.

title: Fusion of Near Infrared Face and Iris Biometrics
abstract: In this paper, we present a method for fusing face and iris biometrics using single near infrared (NIR) image. Fusion of NIR face and iris modalities is a natural way of doing multi-model biometrics because they can be acquired in a single image. An NIR face image is taken using a high resolution NIR camera. Face and iris are segmented from the same NIR image. Face and iris features are then extracted from the segmented parts. Matching of face and iris is done using the respective features. The matching scores are fused using various rules. Experiments give promising results.

title: Outdoor Face Recognition Using Enhanced Near Infrared Imaging
abstract: In this paper, we present a robust and accurate system for outdoor (as well as indoor) face recognition, based on a recently developed enhanced near-infrared (ENIR) imaging device. Using a narrow band NIR laser generator instead of LED lights for active frontal illumination, the ENIR device can provide face images of good quality even under sunlight. Experiments show that the ENIR system performs similarly to the existing NIR system when used indoors, but outperforms it significantly outdoors especially under sunlight.

title: Perceptually-motivated shape exaggeration
abstract: Image communication would appear more efficient if the visual cases of the concerned parts can be enhanced. One way to achieve this is by local shape exaggeration in rendering. In this paper, we present an interactive scheme for controllable local shape exaggeration. Our approach achieves local, direct, and consistent appearance enhancement by modifying the surface orientation in an intuitive and globally optimized manner with sparse user-specified constraints. Compared with previous approaches, the main contribution of this paper is the introduction of adaptive exaggeration function (AEF), which is capable of modulating the extent of detail enhancement to obtain a satisfactory shape exaggeration result. The AEF model is derived based on a series of experiments. We complement our new approach with a variety of examples, user studies, and provide comparisons with recent approaches.

title: Transaction Time Support Inside a Database Engine
abstract: Transaction time databases retain and provide access to prior states of a database. An update "inserts" a new record while preserving the old version. Immortal DB builds transaction time database support into a database engine, not in middleware. It supports as of queries returning records current at the specified time. It also supports snapshot isolation concurrency control. Versions are stamped with the "clock times" of their updating transactions. The timestamp order agrees with transaction serialization order. Lazy timestamping propagates timestamps to transaction updates after commit. Versions are kept in an integrated storage structure, with historical versions initially stored with current data. Time-splits of pages permit large histories to be maintained, and enable time based indexing, which is essential for high performance historical queries. Experiments show that Immortal DB introduces little overhead for accessing recent database states while providing access to past states.

title: Motivated learning agent model for distributed collaborative systems
abstract: The paper develops and discusses a conceptual model for a Mixed Reality-based remote collaborative system (MR-Collab) based on motivated learning Agents. The proposed MR-Collab system is described in details as well to help better understand this model. Self-Development Agent based on previous work is an intelligent Agent, which not only receives information from sensors in the environment, but also gives valuable suggestions that could help to improve the system. The model is not limited to this specific system, but could be adapted to other collaboration systems as well.

title: Closed-loop cross-layer SDMA designs with outdated CSIT
abstract: In this paper, we propose a novel closed-loop approach for robust downlink multi-antenna cross-layer design with outdated channel state information at the transmitter (CSIT). Based on the ACK/NAK feedbacks from the mobiles, the proposed cross-layer design does not require the knowledge of CSIT error statistics. We formulate the cross-layer design as a mixed combinatorial search and Markov decision process (MDP). While it is well-known that general solutions for MDP are very complex, one important contribution of this paper is that we obtain simple closed-form power, rate and user allocation policies which are asymptotically optimal for small target frame error rate (FER). Simulation results illustrate that the performance of the proposed closed-loop cross-layer design is very robust with respect to the outdated CSIT, CSIT error model mismatch as well as channel variations due to Doppler.

title: Log-based recovery for middleware servers
abstract: We have developed new methods for log-based recovery for middleware servers which involve thread pooling, private in-memory states for clients, shared in-memory state and message interactions among middleware servers. Due to the observed rareness of crashes, relatively small size of shared state and infrequency of shared state read/write accesses, we are able to reduce the overhead of message logging and shared state logging while maintaining recovery independence. Checkpointing has a very small impact on ongoing activities while still reducing recovery time. Our recovery mechanism enables client private states to be recovered in parallel after a crash. On a commercial middleware server platform, we have implemented a recovery infrastructure prototype, which demonstrates the manageability of system complexity and shows promising performance results.