Abstract-Gait as a behavioral biometric has been the subject of recent investigations. However, understanding the limits of gait-based recognition and the quantitative study of the factors effecting gait have been confounded by errors in the extracted silhouettes, upon which most recognition algorithms are based. To enable us to study this effect on a large population of subjects, we present a novel model based silhouette reconstruction strategy, based on a population based hidden Markov model (HMM), coupled with an eigen-stance model, to correct for common errors in silhouette detection arising from shadows and background subtraction. The model is trained and benchmarked using manually specified silhouettes for 71 subjects from the recently formulated HumanID Gait Challenge database. Unlike other essentially pixel-level silhouette cleaning methods, this method can remove shadows, especially between feet for the legs-apart stance, and remove parts due to any objects being carried, such as briefcase or a walking cane. After quantitatively establishing the improved quality of the silhouette over simple background subtraction, we show on the 122 subjects HumanID Gait Challenge Dataset and using two gait recognition algorithms that the observed poor performance of gait recognition for hard problems involving matching across factors such as surface, time, and shoe are not due to poor silhouette quality, beyond what is available from statistical background subtraction based methods.
We explore the possibility of using both face and gait in enhancing human recognition at a distance performance in outdoor conditions. Although the individual performance of gait and face based biometrics at a distance under outdoor illumination conditions, walking surface changes, and time variations are poor, we show that recognition performance is significantly enhanced by combination of face and gait. For gait, we present a new recognition scheme that relies on computing distances based on selected, discriminatory, gait stances. Given a gait sequence, covering multiple gait cycles, it identifies the salient stances using a population hidden Markov model (HMM). An averaged representation of the detected silhouettes for these stances are then built using eigenstance shape models. Similarity between two gait sequences is based on the similarities of these averaged representations of the salient stances. This gait recognition strategy, which essentially emphasizes shape over dynamics, significantly outperforms the HumanID Gait Challenge baseline algorithm. For face, which is a mature biometric for which many recognition algorithms exists, we chose the elastic bunch graph matching based face recognition method. This method was found to be the best in the FERET 2000 studies. On a gallery database of 70 individuals and two probe sets: one with 39 individuals taken on the same day and the other with 21 individuals taken at least 3 months apart, results indicate that although the verification rate at 1% false alarm rate of individual biometrics are low, their combination performs better. Specifically, for data taken on the same day, individual verification rates are 42% and 40% for face and gait, respectively, but is 73% for their combination. Similarly, for the data taken with at least 3 months apart, the verification rates are 48% and 25% for face and gait, respectively, but is 60% for their combination. We also find that the combination of outdoor gait and one outdoor face per person is superior to using two outdoor face probes per person or using two gait probes per person, which can considered to be statistical controls for showing improvement by biometric fusion.
Intraflagellar transport protein 88 (Ift88) is required for ciliogenesis and shear stress-induced dissolution of cilia in embryonic endothelial cells coincides with endothelial-to-mesenchymal transition (EndMT) in the developing heart. EndMT is also suggested to underlie heart and lung fibrosis, however, the mechanism linking endothelial Ift88, its effect on EndMT and organ fibrosis remains mainly unexplored. We silenced Ift88 in endothelial cells (ECs) in vitro and generated endothelial cell-specific Ift88-knockout mice (Ift88 endo ) in vivo to evaluate EndMT and its contribution towards organ fibrosis, respectively. Ift88-silencing in ECs led to mesenchymal cells-like changes in endothelial cells. The expression level of the endothelial markers (CD31, Tie-2 and VE-cadherin) were significantly reduced with a concomitant increase in the expression level of mesenchymal markers (αSMA, N-Cadherin and FSP-1) in Ift88silenced ECs. Increased EndMT was associated with increased expression of profibrotic Collagen I expression and increased proliferation in Ift88-silenced ECs. Loss of Ift88 in ECs was further associated with increased expression of Sonic Hedgehog signaling effectors. In vivo, endothelial cells isolated from the heart and lung of Ift88 endo mice demonstrated loss of Ift88 expression in the endothelium. The Ift88 endo mice were born in expected Mendelian ratios without any adverse cardiac phenotypes at baseline. Cardiac and pulmonary endothelial cells isolated from the Ift88 endo mice demonstrated signs of EndMT and bleomycin treatment exacerbated pulmonary fibrosis in Ift88 endo mice. Pressure overload stress in the form of aortic banding did not reveal a significant difference in cardiac fibrosis between Ift88 endo mice and control mice. Our findings demonstrate a novel association between endothelial cilia with EndMT and cell proliferation and also show that loss of endothelial cilia-associated increase in EndMT contributes specifically towards pulmonary fibrosis.Primary cilia (singular: cilium) are short (1-3 μm) hair-like projections arising from the surface of almost every vertebrate cell. Cilia are not completely bound by the plasma membrane but they represent a spatially distinct compartment separated from the rest of the cell 1 . Cilia depend upon an evolutionarily conserved mechanism of a microtubule-based transport system called intraflagellar transport (IFT) for its maintenance and function 2 . Deletion of intraflagellar transport 88 (Ift88) results in loss of cilia 3 . Endothelial cells (ECs) line the heart and blood vessels and, as a result, are constantly exposed to hemodynamic forces. The endothelium is very sensitive to fluctuations in these dynamic physical and chemical conditions and, under physiological conditions, responds accordingly by releasing autocrine and paracrine factors 4 . The differential EC response to constantly varying flow patterns requires accurate mechanotransduction and mechanosensing 4 . In adult vasculature, primary cilia are located at atherosclerotic-prone sites wh...
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Background: SARA promotes an epithelial cell phenotype, whereas its down-regulation is permissive for EMT. Results: PI3K inhibition decreases SARA protein expression, likely through alterations in Rab5-containing endosomes. Conclusion: PI3K signaling supports an epithelial phenotype. Significance: PI3K has complex effects in fibrogenesis. Our data suggest an antifibrotic action of PI3K that involves maintaining SARA expression.
Most state of the art video-based gait recognition algorithms start from binary silhouettes. These silhouettes, defined as foreground regions, are usually detected by background subtraction methods, which results in holes or missed parts due to similarity of foreground and background color, and boundary errors due to video compression artifacts. Errors in low-level representation make it hard to understand the effect of certain conditions, such as surface and time, on gait recognition. In this paper, we present a part-level, manual silhouette database consisting of 71 subjects, over one gait cycle, with differences in surface, shoe-type, carrying condition, and time. We have a total of about 11,000 manual silhouette frames. The purpose of this manual silhouette database is twofold. First, this is a resource that we make available at www.GaitChallenge.org for use by the gait community to test and design better silhouette detection algorithms. These silhouettes can also be used to learn gait dynamics. Second, using the baseline gait recognition algorithm, which was specified along with the HumanID Gait Challenge problem, we show that performance from manual silhouettes is similar and only sometimes better than that from automated silhouettes detected by statistical background subtraction. Low performances when comparing sequences with differences in walking surfaces and time-variation are not fully explained by silhouette quality. We also study the recognition power in each body part and show that recognition based on just the legs is equal to that from the whole silhouette. There is also significant recognition power in the head and torso shape.
Abstract-Potential sources for gait biometrics can be seen to derive from two aspects: gait shape and gait dynamics. We show that improved gait recognition can be achieved after normalization of dynamics and focusing on the shape information. We normalize for gait dynamics using a generic walking model, as captured by a population Hidden Markov Model (pHMM) defined for a set of individuals. The states of this pHMM represent gait stances over one gait cycle and the observations are the silhouettes of the corresponding gait stances. For each sequence, we first use Viterbi decoding of the gait dynamics to arrive at one dynamics-normalized, averaged, gait cycle of fixed length. The distance between two sequences is the distance between the two corresponding dynamics-normalized gait cycles, which we quantify by the sum of the distances between the corresponding gait stances. Distances between two silhouettes from the same generic gait stance are computed in the linear discriminant analysis space so as to maximize the discrimination between persons, while minimizing the variations of the same subject under different conditions. The distance computation is constructed so that it is invariant to dilations and erosions of the silhouettes. This helps us handle variations in silhouette shape that can occur with changing imaging conditions. We present results on three different, publicly available, data sets. First, we consider the HumanID Gait Challenge data set, which is the largest gait benchmarking data set that is available (122 subjects), exercising five different factors, i.e., viewpoint, shoe, surface, carrying condition, and time. We significantly improve the performance across the hard experiments involving surface change and briefcase carrying conditions. Second, we also show improved performance on the UMD gait data set that exercises time variations for 55 subjects. Third, on the CMU Mobo data set, we show results for matching across different walking speeds. It is worth noting that there was no separate training for the UMD and CMU data sets.
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