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

Developmental diversity in free-living flatworms

Flatworm embryology has attracted attention since the early beginnings of comparative evolutionary biology. Considered for a long time the most basal bilaterians, the Platyhelminthes (excluding Acoelomorpha) are now robustly placed within the Spiralia. Despite having lost their relevance to explain the transition from radially to bilaterally symmetrical animals, the study of flatworm embryology is still of great importance to understand the diversification of bilaterians and of developmental mechanisms. Flatworms are acoelomate organisms generally with a simple centralized nervous system, a blind gut, and lacking a circulatory organ, a skeleton and a respiratory system other than the epidermis. Regeneration and asexual reproduction, based on a totipotent neoblast stem cell system, are broadly present among different groups of flatworms. While some more basally branching groups - such as polyclad flatworms - retain the ancestral quartet spiral cleavage pattern, most flatworms have significantly diverged from this pattern and exhibit unique strategies to specify the common adult body plan. Most free-living flatworms (i.e. Platyhelminthes excluding the parasitic Neodermata) are directly developing, whereas in polyclads, also indirect developers with an intermediate free-living larval stage and subsequent metamorphosis are found. A comparative study of developmental diversity may help understanding major questions in evolutionary biology, such as the evolution of cleavage patterns, gastrulation and axial specification, the evolution of larval types, and the diversification and specialization of organ systems. In this review, we present a thorough overview of the embryonic development of the different groups of free-living (turbellarian) platyhelminths, including the Catenulida, Macrostomorpha, Polycladida, Lecithoepitheliata, Proseriata, Bothrioplanida, Rhabdocoela, Fecampiida, Prolecithophora and Tricladida, and discuss their main features under a consensus phylogeny of the phylum

Approximating Intersections and Differences Between Linear Statistical Shape Models Using Markov Chain Monte Carlo

To date, the comparison of Statistical Shape Models (SSMs) is often solely performance-based, carried out by means of simplistic metrics such as compactness, generalization, or specificity. Any similarities or differences between the actual shape spaces can neither be visualized nor quantified. In this paper, we present a new method to qualitatively compare two linear SSMs in dense correspondence by computing approximate intersection spaces and set-theoretic differences between the (hyper-ellipsoidal) allowable shape domains spanned by the models. To this end, we approximate the distribution of shapes lying in the intersection space using Markov chain Monte Carlo and subsequently apply Principal Component Analysis (PCA) to the posterior samples, eventually yielding a new SSM of the intersection space. We estimate differences between linear SSMs in a similar manner; here, however, the resulting spaces are no longer convex and we do not apply PCA but instead use the posterior samples for visualization. We showcase the proposed algorithm qualitatively by computing and analyzing intersection spaces and differences between publicly available face models, focusing on gender-specific male and female as well as identity and expression models. Our quantitative evaluation based on SSMs built from synthetic and real-world data sets provides detailed evidence that the introduced method is able to recover ground-truth intersection spaces and differences accurately.Comment: Accepted to WACV'2

Free-living flatworms under the knife: past and present

Traditionally, regeneration research has been closely tied to flatworm research, as flatworms (Plathelminthes) were among the first animals where the phenomenon of regeneration was discovered. Since then, the main focus of flatworm regeneration research was on triclads, for which various phenomena were observed and a number of theories developed. However, free-living flatworms encompass a number of other taxa where regeneration was found to be possible. This review aims to display and to compare regeneration in all major free-living flatworm taxa, with special focus on a new player in the field of regeneration, Macrostomum lignano (Macrostomorpha). Findings on the regeneration capacity of this organism provide clues for links between regeneration and (post-)embryonic development, starvation, and asexual reproduction. The role of the nervous system and especially the brain for regeneration is discussed, and similarities as well as particularities in regeneration among free-living flatworms are pointed out

Semantic Morphable Models

In this thesis we discuss how computers can automatically interpret images of human faces. The applications of face image analysis systems range from image description, face analysis, interpretation, human-computer interaction, forensics to image manipulation. The analysis of faces in unconstrained scenes is a challenging task. Faces appear in images in a high variety of shape and texture and factors influencing the image formation process like illumination, 3D pose and the scene itself. A face is only a component of a scene and can be occluded by glasses or various other objects in front of the face. We propose an attribute-based image description framework for the analysis of unconstrained face images. The core of the framework are copula Morphable Models to jointly model facial shape, color and attributes in a generative statistical way. A set of model parameters for a face image directly holds facial attributes as image description. We estimate the model parameters for a new image in an Analysis-by-Synthesis setting. In this process, we include a semantic segmentation of the target image into semantic regions to be targeted by their associated models. Different models compete to explain the image pixels. We focus on face image analysis and use a face, a beard and a non-face model to explain different parts of input images. This semantic Morphable Model framework leads to better face explanation since only pixels belonging to the face have to be explained by the face model. We include occlusions or beards as semantic regions and model them as separated classes in the implemented application of the proposed framework. A main cornerstone for the Analysis-by-Synthesis process is illumination estimation. Illumination dominates facial appearance and varies strongly in natural images. We explicitly estimate the illumination condition robust to occlusions and outliers. This thesis combines copula Morphable Models, semantic model adaptation, image segmentation and robust illumination estimation which are necessary to build the overall semantic Morphable Model framework

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

Numerical Investigation of a Forced-Air Cooled Condenser Using 1d-3d-Coupling

To further improve the efficiency of refrigerators and freezers, it is necessary to optimize the whole cooling cycle. With one-dimensional numerical simulation programs, the transient behavior of the refrigeration cycle over more than 24 hours can be calculated. A major challenge here is to find appropriate heat transfer coefficients especially when the heat transfer is mainly influenced by forced-ventilation. In the present paper a refrigerator with a forced-air cooled condenser is investigated. To take the influences of the air flow and the condenser geometry on the simulated heat transfer into consideration, three-dimensional flow simulations are used. Due to superheating, subcooling and the transient behavior, the temperature varies across the condenser. Since the transferred heat strongly depends on the distribution of the temperature across the condenser surface, it would be necessary to run the 3d-CFD-simulation after every time step of the 1-d-simulation. To avoid this tremendous numerical effort, a more efficient method with so-called factors of influence is introduced. These factors of influence are calculated, using the results of around 30 CFD-simulations, which are performed with different temperature distributions. After the determination of these factors, the heat transfer for a given condenser geometry can be calculated for different temperature distributions with a simple algebraic equation. To validate this procedure, CFD-simulations with random temperature distributions are performed and compared with the results of the new method. The developed method has been implemented into the 1d-cycle-simulation. Thus it is possible to consider the geometry of the condenser and the complex flow field in the 1d-simulation without a noticeable increase of the computational effort. Finally the results of the cycle simulation are validated with measurement results