8593487
Behavioral Consistency Extraction for Face Verification
According to the preferred embodiment of the present invention pull-up/pull-down circuits are provided that use transistors with different threshold voltages to assure power-up to the correct predetermined state. These circuits have the ability to hold a node up or down while drawing very little DC current. In one embodiment a pull-up/pull-down circuit is provided that powers up to a first state with the pull-up node high and the pull-down node low, and that can be toggled from one state to another. A second embodiment provides a pull-up or pull-down circuit that powers up to the desired state and can be disabled by pulling the pull-up node low or pulling the pull-down node high. The circuits remain disabled until the power to the circuit is cycled.
1612186
3D facial geometry recovery via group-wise optical flow
We describe an algorithm for automatically finding correspondences from face video sequences. This method is useful to many applications such as face tracking, face modeling and 3D face recovery. Given a sequence of images, the face feature points are tracked by a model-constraint optical flow algorithm. By employing a minimum description length (MDL) point-refinement framework, the drift-off error caused by the optical flow algorithm can be reduced and the correspondences can be matched robustly by optimizing the statistical model. As a result, the face is able to be tracked precisely. Furthermore, it offers a new method of building an appearance model automatically. The objective root mean square error (RMSE) is used to prove the efficiency of the algorithm. At the same time, the performance is evaluated subjectively by generating 3D face models based upon it.
17735204
3D Face Reconstruction Under Imperfect Tracking Circumstances Using Shape Model Constraints
We propose a factorization structure from motion (SfM) framework which employs 3D active shape constraints for a 3D face model application. Two types of shape model, individual shape models and a generic model, are used to approximate non-linear manifold variation. When the 3D shape models are trained, they help the SfM algorithm to reconstruct the 3D face structure under noisy observation (tracking) circumstances. By minimizing two sets of errors, the reconstruction error generated by the linear transform of the shape models and projection error obtained by re-projecting the 3D shape to 2D positions, the 3D face shapes can be recovered optimally. Experimental results show that this algorithm accurately reconstructs the 3D shape of familiar and non-familiar faces from video sequences under circumstances of imperfect face tracking or noisy observations.
1979571
Face Video Competition
Person recognition using facial features, e.g., mug-shot images, has long been used in identity documents. However, due to the widespread use of web-cams and mobile devices embedded with a camera, it is now possible to realise facial video recognition, rather than resorting to just still images. In fact, facial video recognition offers many advantages over still image recognition; these include the potential of boosting the system accuracy and deterring spoof attacks. This paper presents the first known benchmarking effort of person identity verification using facial video data. The evaluation involves 18 systems submitted by seven academic institutes.
13944296
Textureshop: texture synthesis as a photograph editing tool
We combine existing techniques for shape-from-shading and texture synthesis to create a new tool for texturing objects in photographs. Our approach clusters pixels with similar recovered normals into patches on which texture is synthesized. Distorting the texture based on the recovered normals creates the illusion that the texture adheres to the undulations of the photographed surface. Inconsistencies in the recovered surface are disguised by the graphcut blending of the individually textured patches. Further applications include the generation of detail on manually-shaded painting, extracting and synthesizing a displacement map from a texture swatch, and the embossed transfer of normals from one image to another, which would be difficult to create with current image processing packages.
8868065
Controlling Uncertainty in Personal Positioning at Minimal Measurement Cost
One interesting scenario in personal positioning involves an energy-conscious mobile user who tries to obtain estimates about his positions with sufficiently high confidence while consuming as little battery energy as possible. Besides obtaining estimates directly from a position measuring device, the user can rely on extrapolative calculations based on a user movement model and a known initial estimate. Because each measuring probe usually incurs a substantially higher cost than the extrapolative calculation, the objective is to minimize the overall cost of the measurement probes. 
 
Assuming that the user moves at a normally-distributed velocity, we consider two scenarios which differ in the probing devices used. In the first scenario, only one probing device is used. In this case, the aim is to minimize the total number of probes required. In the second scenario, two types of positioning devices are given, where one type of devices offers a higher positioning precision, but also at a greater probing cost. In this case, the aim is to choose an optimal combination of probes from the two types of devices. 
 
For both scenarios, we present algorithms for determining the minimum-cost probing sequences. The algorithms are computationally efficient in reducing the searching space of all possible probing sequences. Our approach is based on Kalman Filtering theory which allows to integrate estimates obtained from the measurements and the extrapolative calculations. The variances in the estimates can provably stay below the specified level throughout the journey. 
 
To the best of our knowledge, these results appear to be the first that uses a mathematically rigorous approach to minimize the probing cost while guaranteeing the quality of estimates in personal positioning.
14779811
Semantic term matching in axiomatic approaches to information retrieval
A common limitation of many retrieval models, including the recently proposed axiomatic approaches, is that retrieval scores are solely based on exact (i.e., syntactic) matching of terms in the queries and documents, without allowing distinct but semantically related terms to match each other and contribute to the retrieval score. In this paper, we show that semantic term matching can be naturally incorporated into the axiomatic retrieval model through defining the primitive weighting function based on a semantic similarity function of terms. We define several desirable retrieval constraints for semantic term matching and use such constraints to extend the axiomatic model to directly support semantic term matching based on the mutual information of terms computed on some document set. We show that such extension can be efficiently implemented as query expansion. Experiment results on several representative data sets show that, with mutual information computed over the documents in either the target collection for retrieval or an external collection such as the Web, our semantic expansion consistently and substantially improves retrieval accuracy over the baseline axiomatic retrieval model. As a pseudo feedback method, our method also outperforms a state-of-the-art language modeling feedback method.
1454940
Video Indexing and Retrieval in Compressed Domain Using Fuzzy-Categorization
There has been an increased interest in video indexing and retrieval in recent years. In this work, indexing and retrieval system of the visual contents is based on feature extracted from the compressed domain. Direct possessing of the compressed domain spares the decoding time, which is extremely important when indexing large number of multimedia archives. A fuzzy-categorizing structure is designed in this paper to improve the retrieval performance. In our experiment, a database that consists of basketball videos has been constructed for our study. This database includes three categories: full-court match, penalty and close-up. First, spatial and temporal feature extraction is applied to train the fuzzy membership functions using the minimum entropy optimal algorithm. Then, the max composition operation is used to generate a new fuzzy feature to represent the content of the shots. Finally, the fuzzy-based representation becomes the indexing feature for the content-based video retrieval system. The experimental results show that the proposal algorithm is quite promising for semantic-based video retrieval.
7764302
Cognitive personal positioning based on activity map and adaptive particle filter
This paper presents a cognitive approach for a reliable yet battery-friendly personal positioning. A user's position is learned from both historical log and possible measurements. Firstly, user's past activities recorded in the log are summarized into an activity map. Accordingly, a user-habit guided particle filtering algorithm is presented for position prediction. Specifically, our algorithm makes reference to the map to determine the most probable correct position, smoothed with occasional measurement. User's current position is modeled probabilistically by a collection of particles and her future moves are modeled with a tendency to follow a familiar path on the map; The estimate is then smoothed by Bayesian filtering. We also allow the number of particles to vary according to user's position in the map. Thus, along with better insights about user's movement experience, our approach can learn from the past and potentially improve the quality of estimates. Our experiments show that this adaptive filtering model using the activity map can deal with non-linear behaviors rather effectively. The new cognitive scheme can indeed track the user's position with a high degree of accuracy. Moreover, the algorithms exhibit low computational complexities, making them well suited for applications on wearable computers.