Face recognition by means of advanced contributions in machine learning

Face recognition (FR) has been extensively studied, due to both scientific fundamental challenges and current and potential applications where human identification is needed. FR systems have the benefits of their non intrusiveness, low cost of equipments and no useragreement requirements when doing acquisition, among the most important ones. Nevertheless, despite the progress made in last years and the different solutions proposed, FR performance is not yet satisfactory when more demanding conditions are required (different viewpoints, blocked effects, illumination changes, strong lighting states, etc). Particularly, the effect of such non-controlled lighting conditions on face images leads to one of the strongest distortions in facial appearance. This dissertation addresses the problem of FR when dealing with less constrained illumination situations. In order to approach the problem, a new multi-session and multi-spectral face database has been acquired in visible, Near-infrared (NIR) and Thermal infrared (TIR) spectra, under different lighting conditions. A theoretical analysis using information theory to demonstrate the complementarities between different spectral bands have been firstly carried out. The optimal exploitation of the information provided by the set of multispectral images has been subsequently addressed by using multimodal matching score fusion techniques that efficiently synthesize complementary meaningful information among different spectra. Due to peculiarities in thermal images, a specific face segmentation algorithm has been required and developed. In the final proposed system, the Discrete Cosine Transform as dimensionality reduction tool and a fractional distance for matching were used, so that the cost in processing time and memory was significantly reduced. Prior to this classification task, a selection of the relevant frequency bands is proposed in order to optimize the overall system, based on identifying and maximizing independence relations by means of discriminability criteria. The system has been extensively evaluated on the multispectral face database specifically performed for our purpose. On this regard, a new visualization procedure has been suggested in order to combine different bands for establishing valid comparisons and giving statistical information about the significance of the results. This experimental framework has more easily enabled the improvement of robustness against training and testing illumination mismatch. Additionally, focusing problem in thermal spectrum has been also addressed, firstly, for the more general case of the thermal images (or thermograms), and then for the case of facialthermograms from both theoretical and practical point of view. In order to analyze the quality of such facial thermograms degraded by blurring, an appropriate algorithm has been successfully developed. Experimental results strongly support the proposed multispectral facial image fusion, achieving very high performance in several conditions. These results represent a new advance in providing a robust matching across changes in illumination, further inspiring highly accurate FR approaches in practical scenarios.El reconeixement facial (FR) ha estat àmpliament estudiat, degut tant als reptes fonamentals científics que suposa com a les aplicacions actuals i futures on requereix la identificació de les persones. Els sistemes de reconeixement facial tenen els avantatges de ser no intrusius,presentar un baix cost dels equips d’adquisició i no la no necessitat d’autorització per part de l’individu a l’hora de realitzar l'adquisició, entre les més importants. De totes maneres i malgrat els avenços aconseguits en els darrers anys i les diferents solucions proposades, el rendiment del FR encara no resulta satisfactori quan es requereixen condicions més exigents (diferents punts de vista, efectes de bloqueig, canvis en la il·luminació, condicions de llum extremes, etc.). Concretament, l'efecte d'aquestes variacions no controlades en les condicions d'il·luminació sobre les imatges facials condueix a una de les distorsions més accentuades sobre l'aparença facial. Aquesta tesi aborda el problema del FR en condicions d'il·luminació menys restringides. Per tal d'abordar el problema, hem adquirit una nova base de dades de cara multisessió i multiespectral en l'espectre infraroig visible, infraroig proper (NIR) i tèrmic (TIR), sota diferents condicions d'il·luminació. En primer lloc s'ha dut a terme una anàlisi teòrica utilitzant la teoria de la informació per demostrar la complementarietat entre les diferents bandes espectrals objecte d’estudi. L'òptim aprofitament de la informació proporcionada pel conjunt d'imatges multiespectrals s'ha abordat posteriorment mitjançant l'ús de tècniques de fusió de puntuació multimodals, capaces de sintetitzar de manera eficient el conjunt d’informació significativa complementària entre els diferents espectres. A causa de les característiques particulars de les imatges tèrmiques, s’ha requerit del desenvolupament d’un algorisme específic per la segmentació de les mateixes. En el sistema proposat final, s’ha utilitzat com a eina de reducció de la dimensionalitat de les imatges, la Transformada del Cosinus Discreta i una distància fraccional per realitzar les tasques de classificació de manera que el cost en temps de processament i de memòria es va reduir de forma significa. Prèviament a aquesta tasca de classificació, es proposa una selecció de les bandes de freqüències més rellevants, basat en la identificació i la maximització de les relacions d'independència per mitjà de criteris discriminabilitat, per tal d'optimitzar el conjunt del sistema. El sistema ha estat àmpliament avaluat sobre la base de dades de cara multiespectral, desenvolupada pel nostre propòsit. En aquest sentit s'ha suggerit l’ús d’un nou procediment de visualització per combinar diferents bandes per poder establir comparacions vàlides i donar informació estadística sobre el significat dels resultats. Aquest marc experimental ha permès més fàcilment la millora de la robustesa quan les condicions d’il·luminació eren diferents entre els processos d’entrament i test. De forma complementària, s’ha tractat la problemàtica de l’enfocament de les imatges en l'espectre tèrmic, en primer lloc, pel cas general de les imatges tèrmiques (o termogrames) i posteriorment pel cas concret dels termogrames facials, des dels punt de vista tant teòric com pràctic. En aquest sentit i per tal d'analitzar la qualitat d’aquests termogrames facials degradats per efectes de desenfocament, s'ha desenvolupat un últim algorisme. Els resultats experimentals recolzen fermament que la fusió d'imatges facials multiespectrals proposada assoleix un rendiment molt alt en diverses condicions d’il·luminació. Aquests resultats representen un nou avenç en l’aportació de solucions robustes quan es contemplen canvis en la il·luminació, i esperen poder inspirar a futures implementacions de sistemes de reconeixement facial precisos en escenaris no controlats.Postprint (published version

Optimal Depth Estimation and Extended Depth of Field from Single Images by Computational Imaging using Chromatic Aberrations

The thesis presents a thorough analysis of a computational imaging approach to estimate the optimal depth, and the extended depth of field from a single image using axial chromatic aberrations. To assist a camera design process, a digital camera simulator is developed which can efficiently simulate different kind of lenses for a 3D scene. The main contribution in the simulator is the fast implementation of space variant filtering and accurate simulation of optical blur at occlusion boundaries. The simulator also includes sensor modeling and digital post processing to facilitate a co-design of optics and digital processing algorithms. To estimate the depth from color images, which are defocused to different amount due to axial chromatic aberrations, a low cost algorithm is developed. Due to varying contrast across colors, a local contrast independent blur measure is proposed. The normalized ratios between the blur measure of all three colors (red, green and blue) are used to estimate the depth for a larger distance range. The analysis of depth errors is performed, which shows the limitations of depth from chromatic aberrations, especially for narrowband object spectra. Since the blur changes over the field and hence depth, therefore, a simple calibration procedure is developed to correct the field varying behavior of estimated depth. A prototype lens is designed with optimal amount of axial chromatic aberrations for a focal length of 4 mm and F-number 2.4. The real captured and synthetic images show the depth measurement with the root mean square error of 10% in the distance range of 30 cm to 2 m. Taking the advantage of chromatic aberrations and estimated depth, a method is proposed to extend the depth of field of the captured image. An imaging sensor with white (W) pixel along with red, green and blue (RGB) pixels with a lens exhibiting axial chromatic aberrations is used to overcome the limitations of previous methods. The proposed method first restores the white image with depth invariant point spread function, and then transfers the sharpness information of the sharpest color or white image to blurred colors. Due to broadband color filter responses, the blur of each RGB color at its focus position is larger in case of chromatic aberrations as compared to chromatic aberrations corrected lens. Therefore, restored white image helps in getting a sharper image for these positions, and also for the objects where the sharpest color information is missing. An efficient implementation of the proposed algorithm achieves better image quality with low computational complexity. Finally, the performance of the depth estimation and extended depth of field is studied for different camera parameters. The criteria are defined to select optimal lens and sensor parameters to acquire desired results with the proposed digital post processing algorithms

Modeling and applications of the focus cue in conventional digital cameras

El enfoque en cámaras digitales juega un papel fundamental tanto en la calidad de la imagen como en la percepción del entorno. Esta tesis estudia el enfoque en cámaras digitales convencionales, tales como cámaras de móviles, fotográficas, webcams y similares. Una revisión rigurosa de los conceptos teóricos detras del enfoque en cámaras convencionales muestra que, a pasar de su utilidad, el modelo clásico del thin lens presenta muchas limitaciones para aplicación en diferentes problemas relacionados con el foco. En esta tesis, el focus profile es propuesto como una alternativa a conceptos clásicos como la profundidad de campo. Los nuevos conceptos introducidos en esta tesis son aplicados a diferentes problemas relacionados con el foco, tales como la adquisición eficiente de imágenes, estimación de profundidad, integración de elementos perceptuales y fusión de imágenes. Los resultados experimentales muestran la aplicación exitosa de los modelos propuestos.The focus of digital cameras plays a fundamental role in both the quality of the acquired images and the perception of the imaged scene. This thesis studies the focus cue in conventional cameras with focus control, such as cellphone cameras, photography cameras, webcams and the like. A deep review of the theoretical concepts behind focus in conventional cameras reveals that, despite its usefulness, the widely known thin lens model has several limitations for solving different focus-related problems in computer vision. In order to overcome these limitations, the focus profile model is introduced as an alternative to classic concepts, such as the near and far limits of the depth-of-field. The new concepts introduced in this dissertation are exploited for solving diverse focus-related problems, such as efficient image capture, depth estimation, visual cue integration and image fusion. The results obtained through an exhaustive experimental validation demonstrate the applicability of the proposed models

Generalising the ideal pinhole model to multi-pupil imaging for depth recovery

This thesis investigates the applicability of computer vision camera models in recovering depth information from images, and presents a novel camera model incorporating a modified pupil plane capable of performing this task accurately from a single image. Standard models, such as the ideal pinhole, suffer a loss of depth information when projecting from the world to an image plane. Recovery of this data enables reconstruction of the original scene as well as object and 3D motion reconstruction. The major contributions of this thesis are the complete characterisation of the ideal pinhole model calibration and the development of a new multi-pupil imaging model which enables depth recovery. A comprehensive analysis of the calibration sensitivity of the ideal pinhole model is presented along with a novel method of capturing calibration images which avoid singularities in image space. Experimentation reveals a higher degree of accuracy using the new calibration images. A novel camera model employing multiple pupils is proposed which, in contrast to the ideal pinhole model, recovers scene depth. The accuracy of the multi-pupil model is demonstrated and validated through rigorous experimentation. An integral property of any camera model is the location of its pupil. To this end, the new model is expanded by generalising the location of the multi-pupil plane, thus enabling superior flexibility over traditional camera models which are confined to positioning the pupil plane to negate particular aberrations in the lens. A key step in the development of the multi-pupil model is the treatment of optical aberrations in the imaging system. The unconstrained location and configuration of the pupil plane enables the determination of optical distortions in the multi-pupil imaging model. A calibration algorithm is proposed which corrects for the optical aberrations. This allows the multi-pupil model to be applied to a multitude of imaging systems regardless of the optical quality of the lens. Experimentation validates the multi-pupil model’s accuracy in accounting for the aberrations and estimating accurate depth information from a single image. Results for object reconstruction are presented establishing the capabilities of the proposed multi-pupil imaging model

Entwurf von Computational-Imaging-Systemen am Beispiel der monokularen Tiefenschätzung

Computational-Imaging-Systeme kombinieren optische und digitale Signalverarbeitung um Information aus dem Licht einer Szene zu extrahieren. In dieser Arbeit wird das Raytracing-Verfahren als Simulationswerkzeug genutzt, um Computational-Imaging-Systeme ganzheitlich zu beschreiben, bewerten und optimieren. Am Beispiel der monokularen Tiefenschätzung wird die Simulation mit einem realen Prototyp einer Kamera mit programmierbarer Apertur verglichen und die vorgestellten Methoden evaluiert