A survey of face detection, extraction and recognition

The goal of this paper is to present a critical survey of existing literatures on human face recognition over the last 4-5 years. Interest and research activities in face recognition have increased significantly over the past few years, especially after the American airliner tragedy on September 11 in 2001. While this growth largely is driven by growing application demands, such as static matching of controlled photographs as in mug shots matching, credit card verification to surveillance video images, identification for law enforcement and authentication for banking and security system access, advances in signal analysis techniques, such as wavelets and neural networks, are also important catalysts. As the number of proposed techniques increases, survey and evaluation becomes important

Data driven analysis of faces from images

This thesis proposes three new data-driven approaches to detect, analyze, or modify faces in images. All presented contributions are inspired by the use of prior knowledge and they derive information about facial appearances from pre-collected databases of images or 3D face models. First, we contribute an approach that extends a widely-used monocular face detector by an additional classifier that evaluates disparity maps of a passive stereo camera. The algorithm runs in real-time and significantly reduces the number of false positives compared to the monocular approach. Next, with a many-core implementation of the detector, we train view-dependent face detectors based on tailored views which guarantee that the statistical variability is fully covered. These detectors are superior to the state of the art on a challenging dataset and can be trained in an automated procedure. Finally, we contribute a model describing the relation of facial appearance and makeup. The approach extracts makeup from before/after images of faces and allows to modify faces in images. Applications such as machine-suggested makeup can improve perceived attractiveness as shown in a perceptual study. In summary, the presented methods help improve the outcome of face detection algorithms, ease and automate their training procedures and the modification of faces in images. Moreover, their data-driven nature enables new and powerful applications arising from the use of prior knowledge and statistical analyses.In der vorliegenden Arbeit werden drei neue, datengetriebene Methoden vorgestellt, die Gesichter in Abbildungen detektieren, analysieren oder modifizieren. Alle Algorithmen extrahieren dabei Vorwissen über Gesichter und deren Erscheinungsformen aus zuvor erstellten Gesichts- Datenbanken, in 2-D oder 3-D. Zunächst wird ein weit verbreiteter monokularer Gesichtsdetektions- Algorithmus um einen zweiten Klassifikator erweitert. In Echtzeit wertet dieser stereoskopische Tiefenkarten aus und führt so zu nachweislich weniger falsch detektierten Gesichtern. Anschließend wird der Basis-Algorithmus durch Parallelisierung verbessert und mit synthetisch generierten Bilddaten trainiert. Diese garantieren die volle Nutzung des verfügbaren Varianzspektrums. So erzeugte Detektoren übertreffen bisher präsentierte Detektoren auf einem schwierigen Datensatz und können automatisch erzeugt werden. Abschließend wird ein Datenmodell für Gesichts-Make-up vorgestellt. Dieses extrahiert Make-up aus Vorher/Nachher-Fotos und kann Gesichter in Abbildungen modifizieren. In einer Studie wird gezeigt, dass vom Computer empfohlenes Make-up die wahrgenommene Attraktivität von Gesichtern steigert. Zusammengefasst verbessern die gezeigten Methoden die Ergebnisse von Gesichtsdetektoren, erleichtern und automatisieren ihre Trainingsprozedur sowie die automatische Veränderung von Gesichtern in Abbildungen. Durch Extraktion von Vorwissen und statistische Datenanalyse entstehen zudem neuartige Anwendungsfelder

Features and statistical classifiers for face image analysis

This thesis presents the systematic analysis of feature spaces and classification schemes for face image processing. Linear discriminants, probabilistic classifiers, and nearest neighbour classifiers are applied to face/nonface classification in various feature spaces including original grayscale space, face-image-whitened space, anything-image-whitened space, and double-whitened space. According to the classification error rates, the probabilistic classifiers performed the best, followed by nearest neighbour classifiers, and then the linear discriminant classifier. However, the former two kinds of classifiers are more computationally demanding. No matter what kind of classifier is used, the whitened space with reduced dimensionality improves classification performance. -- A new feature extraction technique, named dominant feature extraction, is invented and applied to face/nonface classification with encouraging results. This technique extracts the features corresponding to the mean-difference and variance-difference of two classes. Other classification schemes, including the repeated Fisher's Linear Discriminant (FLD) and a moving-centre scheme, are newly proposed and tested. The Maximum Likelihood (ML) classifier based on hyperellipsoid distribution is applied for the first time to face/nonface classification. -- Face images are conventionally represented by grayscales. This work presents a new representation that includes motion vectors, obtained through optical flow analysis between an input image and a neutral template, and a deformation residue that is the difference between the deformed input image and the template. The face images compose a convex cluster in this representation space. The viability of this space is tested and demonstrated through classification experiments on face detection, expression analysis, pose estimation, and face recognition. When the FLD is applied to face/nonface classification and smiling/nonsmiling face classification, the new representation of face images outperforms the traditional grayscale representation. Face recognition experiments using the nearest neighbour classifier on the Olivetti and Oracle Research Laboratory (ORL) face database shows that the deformation residue representation is superior to all other representations. These promising results demonstrate that as a general-purpose space, the derived representation space is suitable for almost all aspects of face image processing

Aeronautical engineering: A continuing bibliography with indexes (supplement 309)

This bibliography lists 212 reports, articles, and other documents introduced into the NASA scientific and technical information system in Oct. 1994. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Wannier90 as a community code: new features and applications

Wannier90 is an open-source computer program for calculating maximally-localised Wannier functions (MLWFs) from a set of Bloch states. It is interfaced to many widely used electronic-structure codes thanks to its independence from the basis sets representing these Bloch states. In the past few years the development of Wannier90 has transitioned to a community-driven model; this has resulted in a number of new developments that have been recently released in Wannier90 v3.0. In this article we describe these new functionalities, that include the implementation of new features for wannierisation and disentanglement (symmetry-adapted Wannier functions, selectively-localised Wannier functions, selected columns of the density matrix) and the ability to calculate new properties (shift currents and Berry-curvature dipole, and a new interface to many-body perturbation theory); performance improvements, including parallelisation of the core code; enhancements in functionality (support for spinor-valued Wannier functions, more accurate methods to interpolate quantities in the Brillouin zone); improved usability (improved plotting routines, integration with high-throughput automation frameworks), as well as the implementation of modern software engineering practices (unit testing, continuous integration, and automatic source-code documentation). These new features, capabilities, and code development model aim to further sustain and expand the community uptake and range of applicability, that nowadays spans complex and accurate dielectric, electronic, magnetic, optical, topological and transport properties of materials.The WDG acknowledges financial support from the NCCR MARVEL of the Swiss National Science Foundation, the European Union’s Centre of Excellence E-CAM (Grant No. 676531), and the Thomas Young Centre for Theory and Simulation of Materials (Grant No. TYC-101).Peer reviewe

Doctor of Philosophy

dissertationPartial differential equations (PDEs) are widely used in science and engineering to model phenomena such as sound, heat, and electrostatics. In many practical science and engineering applications, the solutions of PDEs require the tessellation of computational domains into unstructured meshes and entail computationally expensive and time-consuming processes. Therefore, efficient and fast PDE solving techniques on unstructured meshes are important in these applications. Relative to CPUs, the faster growth curves in the speed and greater power efficiency of the SIMD streaming processors, such as GPUs, have gained them an increasingly important role in the high-performance computing area. Combining suitable parallel algorithms and these streaming processors, we can develop very efficient numerical solvers of PDEs. The contributions of this dissertation are twofold: proposal of two general strategies to design efficient PDE solvers on GPUs and the specific applications of these strategies to solve different types of PDEs. Specifically, this dissertation consists of four parts. First, we describe the general strategies, the domain decomposition strategy and the hybrid gathering strategy. Next, we introduce a parallel algorithm for solving the eikonal equation on fully unstructured meshes efficiently. Third, we present the algorithms and data structures necessary to move the entire FEM pipeline to the GPU. Fourth, we propose a parallel algorithm for solving the levelset equation on fully unstructured 2D or 3D meshes or manifolds. This algorithm combines a narrowband scheme with domain decomposition for efficient levelset equation solving

Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey

Modern communication systems and networks, e.g., Internet of Things (IoT) and cellular networks, generate a massive and heterogeneous amount of traffic data. In such networks, the traditional network management techniques for monitoring and data analytics face some challenges and issues, e.g., accuracy, and effective processing of big data in a real-time fashion. Moreover, the pattern of network traffic, especially in cellular networks, shows very complex behavior because of various factors, such as device mobility and network heterogeneity. Deep learning has been efficiently employed to facilitate analytics and knowledge discovery in big data systems to recognize hidden and complex patterns. Motivated by these successes, researchers in the field of networking apply deep learning models for Network Traffic Monitoring and Analysis (NTMA) applications, e.g., traffic classification and prediction. This paper provides a comprehensive review on applications of deep learning in NTMA. We first provide fundamental background relevant to our review. Then, we give an insight into the confluence of deep learning and NTMA, and review deep learning techniques proposed for NTMA applications. Finally, we discuss key challenges, open issues, and future research directions for using deep learning in NTMA applications.publishedVersio

Tensegrity and Recurrent Neural Networks: Towards an Ecological Model of Postural Coordination

Tensegrity systems have been proposed as both the medium of haptic perception and the functional architecture of motor coordination in animals. However, a full working model integrating those two aspects with some form of neural implementation is still lacking. A basic two-dimensional cross-tensegrity plant is designed and its mechanics simulated. The plant is coupled to a Recurrent Neural Network (RNN). The model’s task is to maintain postural balance against gravity despite the intrinsically unstable configuration of the plant. The RNN takes only proprioceptive input about the springs’ lengths and rate of length change and outputs minimum lengths for each spring which modulates their interaction with the plant’s inertial kinetics. Four artificial agents are evolved to coordinate the patterns of spring contractions in order to maintain dynamic equilibrium. A first study assesses quiet standing performance and reveals coordinative patterns between the tensegrity rods akin to humans’ strategy of anti-phase hip-ankle relative phase. The agents show a mixture of periodic and aperiodic trajectories of their Center of Mass. Moreover, the agents seem to tune to the anticipatory “time-to-balance” quantity in order to maintain their movements within a region of reversibility. A second study perturbs the systems with mechanical platform shifts and sensorimotor degradation. The agents’ response to the mechanical perturbation is robust. Dimensionality analysis of the RNNs’ unit activations reveals a pattern of degree of freedom recruitment after perturbation. In the degradation sub-study, different levels of noise are added to the RNN inputs and different levels of weakening gain are applied to the forces generated by the springs to mimic haptic degradation and muscular weakening in elderly humans. As expected, the systems perform less well, falling earlier than without the insults. However, the same systems re-evolved again under the degraded conditions see significant functional recovery. Overall, the dissertation supports the plausibility of RNN cum tensegrity models of haptics-guided postural coordination in humans