Multilinear Wavelets: A Statistical Shape Space for Human Faces

We present a statistical model for $3$D human faces in varying expression, which decomposes the surface of the face using a wavelet transform, and learns many localized, decorrelated multilinear models on the resulting coefficients. Using this model we are able to reconstruct faces from noisy and occluded $3$D face scans, and facial motion sequences. Accurate reconstruction of face shape is important for applications such as tele-presence and gaming. The localized and multi-scale nature of our model allows for recovery of fine-scale detail while retaining robustness to severe noise and occlusion, and is computationally efficient and scalable. We validate these properties experimentally on challenging data in the form of static scans and motion sequences. We show that in comparison to a global multilinear model, our model better preserves fine detail and is computationally faster, while in comparison to a localized PCA model, our model better handles variation in expression, is faster, and allows us to fix identity parameters for a given subject.Comment: 10 pages, 7 figures; accepted to ECCV 201

Video face replacement

We present a method for replacing facial performances in video. Our approach accounts for differences in identity, visual appearance, speech, and timing between source and target videos. Unlike prior work, it does not require substantial manual operation or complex acquisition hardware, only single-camera video. We use a 3D multilinear model to track the facial performance in both videos. Using the corresponding 3D geometry, we warp the source to the target face and retime the source to match the target performance. We then compute an optimal seam through the video volume that maintains temporal consistency in the final composite. We showcase the use of our method on a variety of examples and present the result of a user study that suggests our results are difficult to distinguish from real video footage.National Science Foundation (U.S.) (Grant PHY-0835713)National Science Foundation (U.S.) (Grant DMS-0739255

Fully Automatic Expression-Invariant Face Correspondence

We consider the problem of computing accurate point-to-point correspondences among a set of human face scans with varying expressions. Our fully automatic approach does not require any manually placed markers on the scan. Instead, the approach learns the locations of a set of landmarks present in a database and uses this knowledge to automatically predict the locations of these landmarks on a newly available scan. The predicted landmarks are then used to compute point-to-point correspondences between a template model and the newly available scan. To accurately fit the expression of the template to the expression of the scan, we use as template a blendshape model. Our algorithm was tested on a database of human faces of different ethnic groups with strongly varying expressions. Experimental results show that the obtained point-to-point correspondence is both highly accurate and consistent for most of the tested 3D face models

International entrepreneurship in SMEs: a study of influencing factors in the textile industry

The final publication is available at Springer via http://dx.doi.org/10.1007/s11365-012-0242-3International entrepreneurship is an incipient research area with a rapidly increasing body of knowledge and contributions. An important part of this literature has focused on the analysis of the contributing factors to IE development. From these studies, this work attempts to analyse and validate through an integrative model the effect on this construct in SME of some of the main factors proposed by the literature such as Skills and Competences, Attitude and Proactiveness, Creativity and Innovation, Networking, Employees and Activity. To proceed with this aim, we conducted an empirical research focused on 174 textile SME in Spain. The results obtained confirm a positive relationship between the studied factors and the IE development. 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RecJ nuclease is required for SOS induction after introduction of a double-strand break in a RecA loading deficient recB mutant of Escherichia coli

The SOS response is an important mechanism which allows Escherichia coli cells to maintain genome integrity. Two key proteins in SOS regulation are LexA (repressor) and RecA (coprotease). The signal for SOS induction is generated at the level of a RecA filament. Depending on the type of DNA damage, a RecA filament is produced by specific activities (helicase, nuclease and RecA loading) of either RecBCD, RecF or a hybrid recombination pathway. It was recently demonstrated that RecA loading activity is essential for the induction of the SOS response after UV-irradiation. In this paper we studied the genetic requirements for SOS induction after introduction of a double-strand break (DSB) by the I-SceI endonuclease in a RecA loading deficient recB mutant (recB1080). We monitored SOS induction by assaying beta-galactosidase activity and compared induction of the response between strains having one or more inactivated mechanisms of RecA loading and their derivatives. We found that simultaneous inactivation of both RecA loading functions (in recB1080 recO double mutant) partially impairs SOS induction after introduction of a DSB. However, we found that the RecJ nuclease is essential for SOS induction after the introduction of a DSB in the recB1080 mutant. This result indicates that RecJ is needed to prepare ssDNA for subsequent loading of RecA protein. It implies that an additional type of RecA loading could exist in the cell