A PCA approach to the object constancy for faces using view-based models of the face

The analysis of object and face recognition by humans attracts a great deal of interest, mainly because of its many applications in various fields, including psychology, security, computer technology, medicine and computer graphics. The aim of this work is to investigate whether a PCA-based mapping approach can offer a new perspective on models of object constancy for faces in human vision. An existing system for facial motion capture and animation developed for performance-driven animation of avatars is adapted, improved and repurposed to study face representation in the context of viewpoint and lighting invariance. The main goal of the thesis is to develop and evaluate a new approach to viewpoint invariance that is view-based and allows mapping of facial variation between different views to construct a multi-view representation of the face. The thesis describes a computer implementation of a model that uses PCA to generate example- based models of the face. The work explores the joint encoding of expression and viewpoint using PCA and the mapping between viewspecific PCA spaces. The simultaneous, synchronised video recording of 6 views of the face was used to construct multi-view representations, which helped to investigate how well multiple views could be recovered from a single view via the content addressable memory property of PCA. A similar approach was taken to lighting invariance. Finally, the possibility of constructing a multi-view representation from asynchronous view-based data was explored. The results of this thesis have implications for a continuing research problem in computer vision – the problem of recognising faces and objects from different perspectives and in different lighting. It also provides a new approach to understanding viewpoint invariance and lighting invariance in human observers

Novel color and local image descriptors for content-based image search

Content-based image classification, search and retrieval is a rapidly-expanding research area. With the advent of inexpensive digital cameras, cheap data storage, fast computing speeds and ever-increasing data transfer rates, millions of images are stored and shared over the Internet every day. This necessitates the development of systems that can classify these images into various categories without human intervention and on being presented a query image, can identify its contents in order to retrieve similar images. Towards that end, this dissertation focuses on investigating novel image descriptors based on texture, shape, color, and local information for advancing content-based image search. Specifically, first, a new color multi-mask Local Binary Patterns (mLBP) descriptor is presented to improve upon the traditional Local Binary Patterns (LBP) texture descriptor for better image classification performance. Second, the mLBP descriptors from different color spaces are fused to form the Color LBP Fusion (CLF) and Color Grayscale LBP Fusion (CGLF) descriptors that further improve image classification performance. Third, a new HaarHOG descriptor, which integrates the Haar wavelet transform and the Histograms of Oriented Gradients (HOG), is presented for extracting both shape and local information for image classification. Next, a novel three Dimensional Local Binary Patterns (3D-LBP) descriptor is proposed for color images by encoding both color and texture information for image search. Furthermore, the novel 3DLH and 3DLH-fusion descriptors are proposed, which combine the HaarHOG and the 3D-LBP descriptors by means of Principal Component Analysis (PCA) and are able to improve upon the individual HaarHOG and 3D-LBP descriptors for image search. Subsequently, the innovative H-descriptor, and the H-fusion descriptor are presented that improve upon the 3DLH descriptor. Finally, the innovative Bag of Words-LBP (BoWL) descriptor is introduced that combines the idea of LBP with a bag-of-words representation to further improve image classification performance. To assess the feasibility of the proposed new image descriptors, two classification frameworks are used. In one, the PCA and the Enhanced Fisher Model (EFM) are applied for feature extraction and the nearest neighbor classification rule for classification. In the other, a Support Vector Machine (SVM) is used for classification. The classification performance is tested on several widely used and publicly available image datasets. The experimental results show that the proposed new image descriptors achieve an image classification performance better than or comparable to other popular image descriptors, such as the Scale Invariant Feature Transform (SIFT), the Pyramid Histograms of visual Words (PHOW), the Pyramid Histograms of Oriented Gradients (PHOG), the Spatial Envelope (SE), the Color SIFT four Concentric Circles (C4CC), the Object Bank (OB), the Hierarchical Matching Pursuit (HMP), the Kernel Spatial Pyramid Matching (KSPM), the SIFT Sparse-coded Spatial Pyramid Matching (ScSPM), the Kernel Codebook (KC) and the LBP

Computer analysis of face beauty: a survey

The human face conveys to other human beings, and potentially to computer systems, information such as identity, intentions, emotional and health states, attractiveness, age, gender and ethnicity. In most cases analyzing this information involves the computer science as well as the human and medical sciences. The most studied multidisciplinary problems are analyzing emotions, estimating age and modeling aging effects. An emerging area is the analysis of human attractiveness. The purpose of this paper is to survey recent research on the computer analysis of human beauty. First we present results in human sciences and medicine pointing to a largely shared and data-driven perception of attractiveness, which is a rationale of computer beauty analysis. After discussing practical application areas, we survey current studies on the automatic analysis of facial attractiveness aimed at: i) relating attractiveness to particular facial features; ii) assessing attractiveness automatically; iii) improving the attractiveness of 2D or 3D face images. Finally we discuss open problems and possible lines of research

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