Face Recognition from Sequential Sparse 3D Data via Deep Registration

Previous works have shown that face recognition with high accurate 3D data is more reliable and insensitive to pose and illumination variations. Recently, low-cost and portable 3D acquisition techniques like ToF(Time of Flight) and DoE based structured light systems enable us to access 3D data easily, e.g., via a mobile phone. However, such devices only provide sparse(limited speckles in structured light system) and noisy 3D data which can not support face recognition directly. In this paper, we aim at achieving high-performance face recognition for devices equipped with such modules which is very meaningful in practice as such devices will be very popular. We propose a framework to perform face recognition by fusing a sequence of low-quality 3D data. As 3D data are sparse and noisy which can not be well handled by conventional methods like the ICP algorithm, we design a PointNet-like Deep Registration Network(DRNet) which works with ordered 3D point coordinates while preserving the ability of mining local structures via convolution. Meanwhile we develop a novel loss function to optimize our DRNet based on the quaternion expression which obviously outperforms other widely used functions. For face recognition, we design a deep convolutional network which takes the fused 3D depth-map as input based on AMSoftmax model. Experiments show that our DRNet can achieve rotation error 0.95{\deg} and translation error 0.28mm for registration. The face recognition on fused data also achieves rank-1 accuracy 99.2% , FAR-0.001 97.5% on Bosphorus dataset which is comparable with state-of-the-art high-quality data based recognition performance.Comment: To be appeared in ICB201

AFFECT-PRESERVING VISUAL PRIVACY PROTECTION

The prevalence of wireless networks and the convenience of mobile cameras enable many new video applications other than security and entertainment. From behavioral diagnosis to wellness monitoring, cameras are increasing used for observations in various educational and medical settings. Videos collected for such applications are considered protected health information under privacy laws in many countries. Visual privacy protection techniques, such as blurring or object removal, can be used to mitigate privacy concern, but they also obliterate important visual cues of affect and social behaviors that are crucial for the target applications. In this dissertation, we propose to balance the privacy protection and the utility of the data by preserving the privacy-insensitive information, such as pose and expression, which is useful in many applications involving visual understanding. The Intellectual Merits of the dissertation include a novel framework for visual privacy protection by manipulating facial image and body shape of individuals, which: (1) is able to conceal the identity of individuals; (2) provide a way to preserve the utility of the data, such as expression and pose information; (3) balance the utility of the data and capacity of the privacy protection. The Broader Impacts of the dissertation focus on the significance of privacy protection on visual data, and the inadequacy of current privacy enhancing technologies in preserving affect and behavioral attributes of the visual content, which are highly useful for behavior observation in educational and medical settings. This work in this dissertation represents one of the first attempts in achieving both goals simultaneously

Driver Face Verification with Depth Maps

Face verification is the task of checking if two provided images contain the face of the same person or not. In this work, we propose a fully-convolutional Siamese architecture to tackle this task, achieving state-of-the-art results on three publicly-released datasets, namely Pandora, High-Resolution Range-based Face Database (HRRFaceD), and CurtinFaces. The proposed method takes depth maps as the input, since depth cameras have been proven to be more reliable in different illumination conditions. Thus, the system is able to work even in the case of the total or partial absence of external light sources, which is a key feature for automotive applications. From the algorithmic point of view, we propose a fully-convolutional architecture with a limited number of parameters, capable of dealing with the small amount of depth data available for training and able to run in real time even on a CPU and embedded boards. The experimental results show acceptable accuracy to allow exploitation in real-world applications with in-board cameras. Finally, exploiting the presence of faces occluded by various head garments and extreme head poses available in the Pandora dataset, we successfully test the proposed system also during strong visual occlusions. The excellent results obtained confirm the efficacy of the proposed method

Development, characterization, and deployment of a high-resolution time-of-flight chemical ionization mass spectrometer (HR-TOF-CIMS) for the detection of carboxylic acids and trace-gas species in the troposphere

Includes bibliographical references.2016 Summer.A historical account of the advances leading to modern high-resolution time-of-flight chemical ionization mass spectrometers (HR-TOF-CIMS) for gas-phase measurements is presented. Recent literature detailing the description of the HR-TOF-CIMS is critically evaluated and put into the context of the historical literature. The development of the HR-TOF-CIMS with reagent ion switching capabilities in the negative mode (acetate and iodide reagent ions), and a novel, low-pressure high-flow inlet with online calibration system is shown to work well in the field. Findings from the deployment of this measurement system during the 2013 Southern Oxidant and Aerosol Study are discussed. Subsequent work with voltage scanning methodologies for controlling cluster transmission is presented and applied to potential aerosol mass chamber experiments examining the oxidation of alpha-pinene. The applicability of acetate chemical ionization to the direct headspace analysis of beer samples is presented. Lastly, the future directions of acetate chemical ionization and voltage scanning are discussed in relation to numerous recent developments related to both gas-phase measurements and new particle formation

Directing Interfacial Events Using Biomimetic Polymer Brushes

Polymer brushes are versatile surface modification tools, wherein composition, architecture and biological functionality can be controlled precisely and independently. By growing biomimetic polymer chains from substrate-bound initiator sites through atom transfer radical polymerization (ATRP), engineered biointerfaces were developed for four application areas. Spatioselective deactivation of ATRP initiator coatings made via chemical vapor deposition polymerization was demonstrated to synthesize micropatterned polymer brushes in a substrate-independent, modular and facile manner. Exposure of 2-bromoisobutyryl groups to UV light resulted in the loss of the bromine atom and effectively inhibited polymer brush growth. Microstructured brushes were selectively grown from those areas on the initiator that were protected from UV exposure, as confirmed by atomic force microscopy (AFM), Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) and imaging ellipsometry. Protein patterns based on specific as well as non-specific adsorption can be created on technologically relevant substrates such as polystyrene, PDMS, polyvinyl chloride and steel. Model surfaces can aid in examining different hypotheses relevant to viral adsorption and formulating design rules for virus-resistant coatings. Thermodynamic models predicted that the extent of viral adsorption is shaped by the interplay between electrostatic attraction offered by binding sites and steric and hydration repulsions arising from surrounding polymer brushes. To verify these predictions, electrostatically heterogeneous carbohydrate-functional brushes were developed. Experimental results confirmed model predictions and offered guidelines for designing virus-resistant surfaces in realistic scenarios where electrostatically attractive defects are prevalent. By allowing the carbohydrate brushes to attain brush thicknesses between 3-5 nm, low levels of protein and viral adsorption could be achieved, even when the defect density was as high as 25-30%. The development of polymeric materials that facilitate the culture of large numbers of human pluripotent stem cells in fully defined conditions, poses a critical engineering challenge. Prior work had indicated that modifying the extent of zwitterionic self-association of PMEDSAH coatings could enhance the propagation rate of human embryonic stem cells (hESCs). Moderately self-associated PMEDSAH coatings were reported to be capable of expanding an initial population of 20,000 hESCS to 4.7 billion pluripotent cells at the end of five weeks, which is 2-fold and 12-fold higher than the estimated propagation rates for unassociated and highly associated coatings respectively. It was hypothesized that a property-prediction tool based on statistical design of experiments could identify reaction parameters that would yield targeted gel architectures. Model predictions were used to decrease the critical thickness at which the wettability transition occurs by merely increasing the catalyst quantity from 1 mol% to 3 mol%. Pro-regenerative M2 macrophages (M2 Mps) have the potential to remediate chronic inflammation in a spectrum of disorders pertaining to macrophage polarization, such as diabetic wounds. By targeting the CD206 receptor on these cells using mannose molecules presented in multivalent architectures, we could engineer coatings that preferentially adhered to M2 cells over pro-inflammatory M1 cells. While a selectivity ratio (for M2 over M1) between 6 to 7 was observed on mannosylated surfaces, the control glucosylated surfaces did not discriminate between M1 and M2 phenotypes, exhibiting a selectivity ratio between 0.4 to 0.7. By applying insights from polymer chemistry, surface science, and thermodynamics, an intimate understanding of biomedically relevant interfacial phenomena was acquired. This enabled the development of a platform based on multifunctional polymer brushes to address diverse problems at the interface of polymers and biology.PHDChemical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144127/1/rmykmr_1.pd

Human body analysis using depth data

Human body analysis is one of the broadest areas within the computer vision field. Researchers have put a strong effort in the human body analysis area, specially over the last decade, due to the technological improvements in both video cameras and processing power. Human body analysis covers topics such as person detection and segmentation, human motion tracking or action and behavior recognition. Even if human beings perform all these tasks naturally, they build-up a challenging problem from a computer vision point of view. Adverse situations such as viewing perspective, clutter and occlusions, lighting conditions or variability of behavior amongst persons may turn human body analysis into an arduous task. In the computer vision field, the evolution of research works is usually tightly related to the technological progress of camera sensors and computer processing power. Traditional human body analysis methods are based on color cameras. Thus, the information is extracted from the raw color data, strongly limiting the proposals. An interesting quality leap was achieved by introducing the multiview concept. That is to say, having multiple color cameras recording a single scene at the same time. With multiview approaches, 3D information is available by means of stereo matching algorithms. The fact of having 3D information is a key aspect in human motion analysis, since the human body moves in a three-dimensional space. Thus, problems such as occlusion and clutter may be overcome with 3D information. The appearance of commercial depth cameras has supposed a second leap in the human body analysis field. While traditional multiview approaches required a cumbersome and expensive setup, as well as a fine camera calibration; novel depth cameras directly provide 3D information with a single camera sensor. Furthermore, depth cameras may be rapidly installed in a wide range of situations, enlarging the range of applications with respect to multiview approaches. Moreover, since depth cameras are based on infra-red light, they do not suffer from illumination variations. In this thesis, we focus on the study of depth data applied to the human body analysis problem. We propose novel ways of describing depth data through specific descriptors, so that they emphasize helpful characteristics of the scene for further body analysis. These descriptors exploit the special 3D structure of depth data to outperform generalist 3D descriptors or color based ones. We also study the problem of person detection, proposing a highly robust and fast method to detect heads. Such method is extended to a hand tracker, which is used throughout the thesis as a helpful tool to enable further research. In the remainder of this dissertation, we focus on the hand analysis problem as a subarea of human body analysis. Given the recent appearance of depth cameras, there is a lack of public datasets. We contribute with a dataset for hand gesture recognition and fingertip localization using depth data. This dataset acts as a starting point of two proposals for hand gesture recognition and fingertip localization based on classification techniques. In these methods, we also exploit the above mentioned descriptor proposals to finely adapt to the nature of depth data.%, and enhance the results in front of traditional color-based methods.L’anàlisi del cos humà és una de les àrees més àmplies del camp de la visió per computador. Els investigadors han posat un gran esforç en el camp de l’anàlisi del cos humà, sobretot durant la darrera dècada, degut als grans avenços tecnològics, tant pel que fa a les càmeres com a la potencia de càlcul. L’anàlisi del cos humà engloba varis temes com la detecció i segmentació de persones, el seguiment del moviment del cos, o el reconeixement d'accions. Tot i que els essers humans duen a terme aquestes tasques d'una manera natural, es converteixen en un difícil problema quan s'ataca des de l’òptica de la visió per computador. Situacions adverses, com poden ser la perspectiva del punt de vista, les oclusions, les condicions d’il•luminació o la variabilitat de comportament entre persones, converteixen l’anàlisi del cos humà en una tasca complicada. En el camp de la visió per computador, l’evolució de la recerca va sovint lligada al progrés tecnològic, tant dels sensors com de la potencia de càlcul dels ordinadors. Els mètodes tradicionals d’anàlisi del cos humà estan basats en càmeres de color. Això limita molt els enfocaments, ja que la informació disponible prové únicament de les dades de color. El concepte multivista va suposar salt de qualitat important. En els enfocaments multivista es tenen múltiples càmeres gravant una mateixa escena simultàniament, permetent utilitzar informació 3D gràcies a algorismes de combinació estèreo. El fet de disposar d’informació 3D es un punt clau, ja que el cos humà es mou en un espai tri-dimensional. Això doncs, problemes com les oclusions es poden apaivagar si es disposa de informació 3D. L’aparició de les càmeres de profunditat comercials ha suposat un segon salt en el camp de l’anàlisi del cos humà. Mentre els mètodes multivista tradicionals requereixen un muntatge pesat i car, i una celebració precisa de totes les càmeres; les noves càmeres de profunditat ofereixen informació 3D de forma directa amb un sol sensor. Aquestes càmeres es poden instal•lar ràpidament en una gran varietat d'entorns, ampliant enormement l'espectre d'aplicacions, que era molt reduït amb enfocaments multivista. A més a més, com que les càmeres de profunditat estan basades en llum infraroja, no pateixen problemes relacionats amb canvis d’il•luminació. En aquesta tesi, ens centrem en l'estudi de la informació que ofereixen les càmeres de profunditat, i la seva aplicació al problema d’anàlisi del cos humà. Proposem noves vies per descriure les dades de profunditat mitjançant descriptors específics, capaços d'emfatitzar característiques de l'escena que seran útils de cara a una posterior anàlisi del cos humà. Aquests descriptors exploten l'estructura 3D de les dades de profunditat per superar descriptors 3D generalistes o basats en color. També estudiem el problema de detecció de persones, proposant un mètode per detectar caps robust i ràpid. Ampliem aquest mètode per obtenir un algorisme de seguiment de mans que ha estat utilitzat al llarg de la tesi. En la part final del document, ens centrem en l’anàlisi de les mans com a subàrea de l’anàlisi del cos humà. Degut a la recent aparició de les càmeres de profunditat, hi ha una manca de bases de dades públiques. Contribuïm amb una base de dades pensada per la localització de dits i el reconeixement de gestos utilitzant dades de profunditat. Aquesta base de dades és el punt de partida de dues contribucions sobre localització de dits i reconeixement de gestos basades en tècniques de classificació. En aquests mètodes, també explotem les ja mencionades propostes de descriptors per millor adaptar-nos a la naturalesa de les dades de profunditat

Ab-initio calculation of the rates of the reactions between volatile organic compounds in wine and cations for mass spectrometry

Manini, NicolaFIRST1AIR UniMi: http://hdl.handle.net/2434/806081openWine is a complex mixture housing many aroma and flavor compounds giving it a unique texture and bouquet. These volatile organic compounds (VOCs), if present near the sensory threshold limits, may contribute positively to wine quality; however, excessive amounts can detract from quality, and are considered as a fault in wine. It is believed that nearly 10% of the world’s wine is affected from various types of faults. The most common and potent wine taint is 2,4,6-trichloroanisole (2,4,6-TCA), commonly known as cork-taint molecule resulting from the cork stopper of wine bottles. 2,4,6- TCA produces intense ’musty’, ’mouldy’ ’earthy’ smelling in wine. Similar off-flavor smells are associated to compounds including geosmin and 2-methoxy3,5-dimethylpyrazine. We studied 74 such VOCs frequently present in wine. Determining concentration of VOCs in wine requires detection techniques to be fast, in real time, and with a detection limit as low as few parts per trillion by volume. The most frequently used techniques based on direct injection mass spectrometry, namely proton transfer reaction mass spectrometry (PTR-MS) and selected ion flow tube mass spectrometry (SIFT-MS), are being successfully employed in the measurements of VOCs concentrations. Quantification using these techniques usually relies on compound-by-compound instrument calibration. The calibration procedures are generally laborious and time consuming. The theoretical evaluation of the rate coefficients of ion-molecule reactions occurring in PTR-MS/SIFT-MS flow (drift) tubes is a practical alternative to calibration. In this thesis, we compute and report the rate coefficients for ion-molecule reactions, relevant for different experimental conditions, such as varied temperature and electric fields inside the drift tube. We have used well-established models based on capture cross-section collision and classical trajectories. These models rely on physical properties such as electric dipole moment and polarizability of the volatile molecules. To compute these quantities we resorted to ab initio density functional theoryopenManjeet, K

Hybrid Single and Dual Pattern Structured Light Illumination

Structured Light Illumination is a widely used 3D shape measurement technique in non-contact surface scanning. Multi-pattern based Structured Light Illumination methods reconstruct 3-D surface with high accuracy, but are sensitive to object motion during the pattern projection and the speed of scanning process is relatively long. To reduce this sensitivity, single pattern techniques are developed to achieve a high speed scanning process, such as Composite Pattern (CP) and Modified Composite Pattern (MCP) technique. However, most of single patter techniques have a significant banding artifact and sacrifice the accuracy. We focus on developing SLI techniques can achieve both high speed, high accuracy and have the tolerance to the relative motion. We first present a novel Two-Pattern Full Lateral Resolution (2PFLR) SLI method utilizing an MCP pattern for non-ambiguous phase followed by a single sinusoidal pattern for high accuracy. The surface phase modulates the single sinusoidal pattern which is demodulated using a Quadrature demodulation technique and then unwrapped by the MCP phase result. A single sinusoidal pattern reconstruction inherently has banding error. To effective de-band the surface, we propose Projector Space De-banding algorithm (PSDb). We use projector space because the band error is aligned with the projector coordinates allowing more accurate estimation of the banding error. 2PFLR system only allows the relative motion within the FOV of the scanner, to extend the application of the SLI, we present the research on Relative Motion 3-D scanner which utilize a single pattern technique. The pattern in RM3D system is designed based on MCP but has white space area to capture the surface texture, and a constellation correlation filter method is used to estimate the scanner\u27s trajectory and then align the 3-D surface reconstructed by each frame to a point cloud of the whole object surface

Synthesis and Energetics of Gold Nanoclusters Tailored by Interfacial Bonding Structure

In addition to the well known quantum confinement effects resulted from size and shape, interfacial bond structure is another factor, affecting the properties of the nanomaterial that is rarely studied. Inspired by the “Au-S-Au” staple motif discovered from the crystal structure of monothiol protected Au102 nanocluster (Science, 2007, 318, 430), dithiol molecules (e. g. 1, 2-dithiol, 1, 4-dithiol, etc.) with molecular structural constraint have been employed to create dithiolate protected clusters or mixed monothiolate and dithiolate protected clusters. The structure and properties of the Au clusters are expected to change due to two effects: The entropy gain of dithiol over monothiol protection and the constraint to the formation of the thiol bridging motif. DMPS (1, 2-dithiol molecule) stabilized clusters with characteristic absorption bands have been obtained, and characterized by multiple techniques. Monolayer reaction on gold core surface between the monothiol tiopronin and dithiol DMPS has been performed, and the mechanism has been probed. Mixed phenylethanethiolate and durene-dithiolate (1, 4-dithiol molecule) protected Au130 clusters with rich electrochemical features have been created, and the optical and electrochemical energetics have been successfully correlated based on core and core-ligand energy states. Furthermore, the impact of 1, 4-dithiolate-Au bonding on the near infrared luminescence has been studied