123451361
Probabilistic expression analysis on manifolds
In this paper, we propose a probabilistic video-based facial expression recognition method on manifolds. The concept of the manifold of facial expression is based on the observation that the images of all possible facial deformations of an individual make a smooth manifold embedded in a high dimensional image space. An enhanced Lipschitz embedding is developed to embed the aligned face appearance in a low dimensional space while keeping the main structure of the manifold. In the embedded space, a complete expression sequence becomes a path on the expression manifold, emanating from a center that corresponds to the neutral expression. Each path consists of several clusters. A probabilistic model of transition between the clusters and paths is learned through training videos in the embedded space. The likelihood of one kind of facial expression is modeled as a mixture density with the clusters as mixture centers. The transition between different expressions is represented as the evolution of the posterior probability of the six basic paths. The experimental results demonstrate that the probabilistic approach can recognize expression transitions effectively. We also synthesize image sequences of changing expressions through the manifold model.
17881375
Virtual Mouse - Inputting Device by Hand Gesture Tracking and Recognition
In this paper, we develop a system to track and recognize hand motion in nearly real time. An important application of this system is to simulate mouse as a visual inputting device. Tracking approach is based on Condensation algorithm, and active shape model. Our contribution is combining multi-modal templates to increase the tracking performance. Weighting value is given to the sampling ratio of Condensation by applying the prior property of the templates. The recognition approach is based on HMM. Experiments show our system is very promising to work as an auxiliary inputting device.
8192681
Image Retrieval Using Schwarz Representation of One-Dimensional Feature
Retrieval efficiency and accuracy are two important issues in designing a content based database retrieval system. In order to retrieve efficiently, we must extract feature to build index. Recently intensive research focused on how to extract one-dimensional features and calculate the distance between them, such as color histogram, Fourier descriptor, image shape spectrum (ISS). We develop a new method to match one-dimensional feature function in multiscale space using Schwarz representation. It can obtain closed form match function and similarity measure instead of traditional optimization. Thus we can calculate the global distance when the local information of feature function is matched. In this paper, we use the center distance function of shape as the feature functions. We calculate their Schwarz representation as indices, and calculate the optimal distance as similarity measure to sort the images. Experimental results show its efficiency and accuracy.
12971605
AAM derived face representations for robust facial action recognition
In this paper, we present results on experiments employing active appearance model (AAM) derived facial representations, for the task of facial action recognition. Experimental results demonstrate the benefit of AAM-derived representations on a spontaneous AU database containing "real-world" variation. Additionally, we explore a number of normalization methods for these representations which increase facial action recognition performance
16132997
Manifold of facial expression
We propose the concept of manifold of facial expression based on the observation that images of a subject's facial expressions define a smooth manifold in the high dimensional image space. Such a manifold representation can provide a unified framework for facial expression analysis. We first apply active wavelet networks (AWN) on the image sequences for facial feature localization. To learn the structure of the manifold in the feature space derived by AWN, we investigated two types of embeddings from a high dimensional space to a low dimensional space: locally linear embedding (LLE) and Lipschitz embedding. Our experiments show that LLE is suitable for visualizing expression manifolds. After applying Lipschitz embedding, the expression manifold can be approximately considered as a super-spherical surface in the embedding space. For manifolds derived from different subjects, we propose a nonlinear alignment algorithm that keeps the semantic similarity of facial expression from different subjects on one generalized manifold. We also show that nonlinear alignment outperforms linear alignment in expression classification.
13451430
Non-negative matrix factorization framework for face recognition
Non-negative Matrix Factorization (NMF) is a part-based image representation method which adds a non-negativity constraint to matrix factorization. NMF is compatible with the intuitive notion of combining parts to form a whole face. In this paper, we propose a framework of face recognition by adding NMF constraint and classifier constraints to matrix factorization to get both intuitive features and good recognition results. Based on the framework, we present two novel subspace methods: Fisher Non-negative Matrix Factorization (FNMF) and PCA Non-negative Matrix Factorization (PNMF). FNMF adds both the non-negative constraint and the Fisher constraint to matrix factorization. The Fisher constraint maximizes the between-class scatter and minimizes the within-class scatter of face samples. Subsequently, FNMF improves the capability of face recognition. PNMF adds the non-negative constraint and characteristics of PCA, such as maximizing the variance of output coordinates, orthogonal bases, etc. to matrix factorization. Therefore, we can get intuitive features and desirable PCA characteristics. Our experiments show that FNMF and PNMF achieve better face recognition performance than NMF and Local NMF.
2350863
Generating and Applying Rules for Interval Valued Fuzzy Observations
One of the objectives of intelligent data engineering and automated learning is to develop algorithms that learn the environment, generate rules, and take possible courses of actions. In this paper, we report our work on how to generate and apply such rules with a rule matrix model. Since the environments can be interval valued and rules often fuzzy, we further study how to obtain and apply rules for interval valued fuzzy observations.
44360962
Parallel Solution of the Poisson-Boltzmann Equation for Proteins
A fortran code PBSOLVE is created for numerical solution of a linear Poissson-Boltzman equation. Finite difference discretization and successive overrelaxation (SOR) iterations on the sequence of grids are used to obtain the approximate electrostatic potential on the grid. Error control is provided by comparison of solution on nested grids. A parallel versioon of the PBSOLVE which exploits the message passing interface (MPI) has also been constructed. The performances of the program and the efficiency of parallelization have been tested on the small peptide Met-Enkephalin.
3771585
Icon-based visualization of large high-dimensional datasets
High dimensional data visualization is critical to data analysts since it gives a direct view of original data. We present a method to visualize large amount of high dimensional data. We divide dimensions of data into several groups. Then, we use one icon to represent each group, and associate visual properties of each icon with dimensions in each group. A high dimensional data record will be represented by multiple different types of icons located in the same position. Furthermore, we use summary icons to display local details of viewer's interests and the whole data set at meantime. We show its effectiveness and efficiency through a case study on a real large data set.
1743563
Toward an evaluation infrastructure for power and energy optimizations
Execution-driven simulators are often used for power/energy and performance evaluation. Simulators can provide semantic details but they provide insufficient speed and accuracy for compiler and OS research. Physical measurement is fast and objective but lacks a semantic connection between the measurement result and the evaluated program. The objective of our research is to bring together the advantages of simulation and physical measurement to build an infrastructure for power and energy optimization. Power and energy behavior is obtained through physical measurement. Simulation is used for observing the connection between power and energy behavior and the evaluated program. Our preliminary results demonstrate the ability of this infrastructure to capture detailed power behavior of any region of a program. To simplify the power/energy evaluation of programs with long execution times and overcome the limitation of physical devices, we propose using the SimPoints methodology developed by researchers at UC San Diego to find representative slices of a program. Through simulation, we validate the feasibility of the SimPoint idea in simplifying power/energy evaluation. We expect that this infrastructure will help researchers in OS/compiler power/energy optimization to evaluate their optimizations more efficiently and observe more optimization opportunities.
863906
Per-Flow Queueing by Dynamic Queue Sharing
Per-flow queuing is believed to be able to guarantee advanced Quality of Service (QoS) for each flow. With the dramatic increase of link speed and number of traffic flows, per-flow queuing faces a great challenge since millions of queues need to be maintained for implementation in a traditional sense. In this paper, by setting only a small number of physical queues, we propose a Dynamic Queue Sharing (DQS) mechanism to achieve an equal performance to the pure per-flow queuing with a lower cost. The proposed mechanism is based on an interesting fact that the number of simultaneous active flows in the router buffer is far less than that of in-progress flows. In DQS, a physical queue is dynamically created on-demand when a new flow comes and then dynamically released when the flow temporarily pauses. Hashing and binary sorting tree (or linked list) are combined to manage the mapping between flows and queues, so as to isolate flows in different queues. Theoretical analysis and traces experiments are conducted to evaluate DQS. The results demonstrate that when the parameters are well set, the operation delay is less than two time cycles in average with an extra memory of 16k bits.