Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems

It is unknown what kind of biases modern in the wild face datasets have because of their lack of annotation. A direct consequence of this is that total recognition rates alone only provide limited insight about the generalization ability of a Deep Convolutional Neural Networks (DCNNs). We propose to empirically study the effect of different types of dataset biases on the generalization ability of DCNNs. Using synthetically generated face images, we study the face recognition rate as a function of interpretable parameters such as face pose and light. The proposed method allows valuable details about the generalization performance of different DCNN architectures to be observed and compared. In our experiments, we find that: 1) Indeed, dataset bias has a significant influence on the generalization performance of DCNNs. 2) DCNNs can generalize surprisingly well to unseen illumination conditions and large sampling gaps in the pose variation. 3) Using the presented methodology we reveal that the VGG-16 architecture outperforms the AlexNet architecture at face recognition tasks because it can much better generalize to unseen face poses, although it has significantly more parameters. 4) We uncover a main limitation of current DCNN architectures, which is the difficulty to generalize when different identities to not share the same pose variation. 5) We demonstrate that our findings on synthetic data also apply when learning from real-world data. Our face image generator is publicly available to enable the community to benchmark other DCNN architectures.Comment: Accepted to CVPR 2018 Workshop on Analysis and Modeling of Faces and Gestures (AMFG

Morphable Face Models - An Open Framework

In this paper, we present a novel open-source pipeline for face registration based on Gaussian processes as well as an application to face image analysis. Non-rigid registration of faces is significant for many applications in computer vision, such as the construction of 3D Morphable face models (3DMMs). Gaussian Process Morphable Models (GPMMs) unify a variety of non-rigid deformation models with B-splines and PCA models as examples. GPMM separate problem specific requirements from the registration algorithm by incorporating domain-specific adaptions as a prior model. The novelties of this paper are the following: (i) We present a strategy and modeling technique for face registration that considers symmetry, multi-scale and spatially-varying details. The registration is applied to neutral faces and facial expressions. (ii) We release an open-source software framework for registration and model-building, demonstrated on the publicly available BU3D-FE database. The released pipeline also contains an implementation of an Analysis-by-Synthesis model adaption of 2D face images, tested on the Multi-PIE and LFW database. This enables the community to reproduce, evaluate and compare the individual steps of registration to model-building and 3D/2D model fitting. (iii) Along with the framework release, we publish a new version of the Basel Face Model (BFM-2017) with an improved age distribution and an additional facial expression model

The Healthy Farms, Food and Communities Act: Policy Initiatives for the 2002 Farm Bill And the First Decade of the 21st Century

This policy document includes a legislative initiative to be incorporated into the 2002 Farm Bill, and a broader set of policy principles and legislation endorsed by CFSC. Both policy platforms create the basis for furthering the goals of healthy farms, healthy food, and, ultimately, healthy communities

Räumliche und zeitliche Visualisierung als Smart-City-Planungswerkzeug

Die steigende Bevölkerung und der starke Zuzug in die urbanen Ballungsräume ist eine große Herausforderung für die Akteure der Planungswelt. Um ressourcenschonende Planungen voranzutreiben, ist eine innere Entwicklung der urbanen Systeme zielführend. Dabei ist neben der Aufspürung und Nutzung von Flächenreserven, die Nutzung und der Ausbau bestehender Versorgungsinfrastruktursysteme eine Möglichkeit für nachhaltige Entwicklungen. Dies stellt eine komplexe Planungsaufgabe für Planer und Entscheidungsträger dar, die das Zusammenwirken von Planungsakteuren unterschiedlichster Domänen erfordert. Innerhalb des interdisziplinären Forschungsprojektes URBEM (Urbanes Energie- und Mobilitätssystem) wurde ein visuelles Planungs- und Entscheidungsunterstützungswerkzeug, die URBEMVisualisierung, entwickelt. Diese webbasierte Umgebung bietet eine Arbeits- und Kommunikationsplattform für Domänenexperten und Stakeholder zur Unterstützung komplexer Planungsprozesse. Die URBEMVisualisierung erlaubt domänenspezifische Simulationsergebnisse räumlich zu verorten, visuelle Übersichten zu generieren und ein urbanes Gesamtsystem mit Hilfe der räumlichen Überlagerung von Informationen unterschiedlichster Versorgungsträgerstrukturen im Bereich Energie und Mobilität zu untersuchen. Dies bietet den Planern eine Grundlage um Probleme im Raum und in der Zeit fest zu machen und gezielte Maßnahmen zur Entwicklung smarter Lebensräume aufzuzeigen. Die Möglichkeiten der URBEM Visualisierung werden im folgenden Beitrag anhand von Modellergebnissen aus der Domäne Mobilität illustriert

Informed MCMC with Bayesian Neural Networks for Facial Image Analysis

Computer vision tasks are difficult because of the large variability in the data that is induced by changes in light, background, partial occlusion as well as the varying pose, texture, and shape of objects. Generative approaches to computer vision allow us to overcome this difficulty by explicitly modeling the physical image formation process. Using generative object models, the analysis of an observed image is performed via Bayesian inference of the posterior distribution. This conceptually simple approach tends to fail in practice because of several difficulties stemming from sampling the posterior distribution: high-dimensionality and multi-modality of the posterior distribution as well as expensive simulation of the rendering process. The main difficulty of sampling approaches in a computer vision context is choosing the proposal distribution accurately so that maxima of the posterior are explored early and the algorithm quickly converges to a valid image interpretation. In this work, we propose to use a Bayesian Neural Network for estimating an image dependent proposal distribution. Compared to a standard Gaussian random walk proposal, this accelerates the sampler in finding regions of the posterior with high value. In this way, we can significantly reduce the number of samples needed to perform facial image analysis.Comment: Accepted to the Bayesian Deep Learning Workshop at NeurIPS 201

Markov Chain Monte Carlo for Automated Face Image Analysis

We present a novel fully probabilistic method to interpret a single face image with the 3D Morphable Model. The new method is based on Bayesian inference and makes use of unreliable image-based information. Rather than searching a single optimal solution, we infer the posterior distribution of the model parameters given the target image. The method is a stochastic sampling algorithm with a propose-and-verify architecture based on the Metropolis–Hastings algorithm. The stochastic method can robustly integrate unreliable information and therefore does not rely on feed-forward initialization. The integrative concept is based on two ideas, a separation of proposal moves and their verification with the model (Data-Driven Markov Chain Monte Carlo), and filtering with the Metropolis acceptance rule. It does not need gradients and is less prone to local optima than standard fitters. We also introduce a new collective likelihood which models the average difference between the model and the target image rather than individual pixel differences. The average value shows a natural tendency towards a normal distribution, even when the individual pixel-wise difference is not Gaussian. We employ the new fitting method to calculate posterior models of 3D face reconstructions from single real-world images. A direct application of the algorithm with the 3D Morphable Model leads us to a fully automatic face recognition system with competitive performance on the Multi-PIE database without any database adaptation

State-Dependent and -Independent Effects of Dialyzing Excitatory Neuromodulator Receptor Antagonists into the Ventral Respiratory Column

Unilateral dialysis of the broad-spectrum muscarinic receptor antagonist atropine (50 mM) into the ventral respiratory column [(VRC) including the pre-Bötzinger complex region] of awake goats increased pulmonary ventilation (V̇i) and breathing frequency (f), conceivably due to local compensatory increases in serotonin (5-HT) and substance P (SP) measured in effluent mock cerebral spinal fluid (mCSF). In contrast, unilateral dialysis of a triple cocktail of antagonists to muscarinic (atropine; 5 mM), neurokinin-1, and 5-HT receptors does not alter V̇i or f, but increases local SP. Herein, we tested hypotheses that 1) local compensatory 5-HT and SP responses to 50 mM atropine dialyzed into the VRC of goats will not differ between anesthetized and awake states; and 2) bilateral dialysis of the triple cocktail of antagonists into the VRC of awake goats will not alter V̇i or f, but will increase local excitatory neuromodulators. Through microtubules implanted into the VRC of goats, probes were inserted to dialyze mCSF alone (time control), 50 mM atropine, or the triple cocktail of antagonists. We found 1) equivalent increases in local 5-HT and SP with 50 mM atropine dialysis during wakefulness compared with isoflurane anesthesia, but V̇i and f only increased while awake; and 2) dialyses of the triple cocktail of antagonists increased V̇i, f, 5-HT, and SP

Changes in Farm Financial Conditions and Farming Practices in Ohio, 1986-1990

Highlights of a five year study of farm households are reported. Two facets of farm households, their financial condition and those farming practices affecting the environment, are analyzed. Results indicate improvements in farm household financial condition, changes to less soil erosive farming practices, but little adoption of low input farming systems