Infrared face recognition: a comprehensive review of methodologies and databases

Automatic face recognition is an area with immense practical potential which includes a wide range of commercial and law enforcement applications. Hence it is unsurprising that it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in face recognition continues to improve, benefitting from advances in a range of different research fields such as image processing, pattern recognition, computer graphics, and physiology. Systems based on visible spectrum images, the most researched face recognition modality, have reached a significant level of maturity with some practical success. However, they continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease recognition accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject. Our key contributions are: (i) a summary of the inherent properties of infrared imaging which makes this modality promising in the context of face recognition, (ii) a systematic review of the most influential approaches, with a focus on emerging common trends as well as key differences between alternative methodologies, (iii) a description of the main databases of infrared facial images available to the researcher, and lastly (iv) a discussion of the most promising avenues for future research.Comment: Pattern Recognition, 2014. arXiv admin note: substantial text overlap with arXiv:1306.160

A novel multispectral and 2.5D/3D image fusion camera system for enhanced face recognition

The fusion of images from the visible and long-wave infrared (thermal) portions of the spectrum produces images that have improved face recognition performance under varying lighting conditions. This is because long-wave infrared images are the result of emitted, rather than reflected, light and are therefore less sensitive to changes in ambient light. Similarly, 3D and 2.5D images have also improved face recognition under varying pose and lighting. The opacity of glass to long-wave infrared light, however, means that the presence of eyeglasses in a face image reduces the recognition performance. This thesis presents the design and performance evaluation of a novel camera system which is capable of capturing spatially registered visible, near-infrared, long-wave infrared and 2.5D depth video images via a common optical path requiring no spatial registration between sensors beyond scaling for differences in sensor sizes. Experiments using a range of established face recognition methods and multi-class SVM classifiers show that the fused output from our camera system not only outperforms the single modality images for face recognition, but that the adaptive fusion methods used produce consistent increases in recognition accuracy under varying pose, lighting and with the presence of eyeglasses

A Survey on Ear Biometrics

Recognizing people by their ear has recently received significant attention in the literature. Several reasons account for this trend: first, ear recognition does not suffer from some problems associated with other non contact biometrics, such as face recognition; second, it is the most promising candidate for combination with the face in the context of multi-pose face recognition; and third, the ear can be used for human recognition in surveillance videos where the face may be occluded completely or in part. Further, the ear appears to degrade little with age. Even though, current ear detection and recognition systems have reached a certain level of maturity, their success is limited to controlled indoor conditions. In addition to variation in illumination, other open research problems include hair occlusion; earprint forensics; ear symmetry; ear classification; and ear individuality. This paper provides a detailed survey of research conducted in ear detection and recognition. It provides an up-to-date review of the existing literature revealing the current state-of-art for not only those who are working in this area but also for those who might exploit this new approach. Furthermore, it offers insights into some unsolved ear recognition problems as well as ear databases available for researchers

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

Multi-Modal Ocular Recognition in presence of occlusion in Mobile Devices

Title from PDF of title page viewed September 18, 2019Dissertation advisor: Reza DerakhshaniVitaIncludes bibliographical references (pages 128-144)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2018The existence eyeglasses in human faces cause real challenges for ocular, facial, and soft-based (such as eyebrows) biometric recognition due to glasses reflection, shadow, and frame occlusion. In this regard, two operations (eyeglasses detection and eyeglasses segmentation) have been proposed to mitigate the effect of occlusion using eyeglasses. Eyeglasses detection is an important initial step towards eyeglass segmentation. Three schemes of eye glasses detection have been proposed which are non-learning-based, learning-based, and deep learning-based schemes. The non-learning scheme of eyeglasses detection which consists of cascaded filters achieved an overall accuracy of 99.0% for VI SOB and 97.9% for FERET datasets. The learning-based scheme of eyeglass detection consisting of extracting Local Binary Pattern (LBP), Histogram of Gradients (HOG) and fusing them together, then applying classifiers (such as Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Linear Discriminant Analysis (LDA)), and fusing the output of these classifiers. The latter obtained a best overall accuracy of about 99.3% on FERET and 100% on VISOB dataset. Besides, the deep learning-based scheme of eye glasses detection showed a comparative study for eyeglasses frame detection using different Convolutional Neural Network (CNN) structures that are applied to Frame Bridge region and extended ocular region. The best CNN model obtained an overall accuracy of 99.96% for ROI consisting of Frame Bridge. Moreover, two schemes of eyeglasses segmentation have been introduced. The first segmentation scheme was cascaded convolutional Neural Network (CNN). This scheme consists of cascaded CNN’s for eyeglasses detection, weight generation, and glasses segmentation, followed by mathematical and binarization operations. The scheme showed a 100% eyeglasses detection and 91% segmentation accuracy by our proposed approach. Also, the second segmentation scheme was the convolutional de-convolutional network. This CNN model has been implemented with main convolutional layers, de-convolutional layers, and one custom (lamda) layer. This scheme achieved better segmentation results of 97% segmentation accuracy over the cascaded approach. Furthermore, two soft biometric re-identification schemes have been introduced with eyeglasses mitigation. The first scheme was eyebrows-based user authentication consists of local, global, deep feature extraction with learning-based matching. The best result of 0.63% EER using score level fusion of handcraft descriptors (HOG, and GIST) with the deep VGG16 descriptor for eyebrow-based user authentication. The second scheme was eyeglass-based user authentication which consisting of eyeglasses segmentation, morphological cleanup, features extraction, and learning-based matching. The best result of 3.44% EER using score level fusion of handcraft descriptors (HOG, and GIST) with the deep VGG16 descriptor for eyeglasses-based user authentication. Also, an EER enhancement of 2.51% for indoor vs. outdoor (In: Out) light set tings was achieved for eyebrow-based authentication after eyeglasses segmentation and removal using Convolutional-Deconvolutional approach followed by in-painting.Introduction -- Background in machine learning and computer vision -- Eyeglasses detection and segmentation -- User authentication using soft-biometric -- Conclusion and future work -- Appendi

State of the Art in Face Recognition

Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state

Data Reduction Algorithms in Machine Learning and Data Science

Raw data are usually required to be pre-processed for better representation or discrimination of classes. This pre-processing can be done by data reduction, i.e., either reduction in dimensionality or numerosity (cardinality). Dimensionality reduction can be used for feature extraction or data visualization. Numerosity reduction is useful for ranking data points or finding the most and least important data points. This thesis proposes several algorithms for data reduction, known as dimensionality and numerosity reduction, in machine learning and data science. Dimensionality reduction tackles feature extraction and feature selection methods while numerosity reduction includes prototype selection and prototype generation approaches. This thesis focuses on feature extraction and prototype selection for data reduction. Dimensionality reduction methods can be divided into three categories, i.e., spectral, probabilistic, and neural network-based methods. The spectral methods have a geometrical point of view and are mostly reduced to the generalized eigenvalue problem. Probabilistic and network-based methods have stochastic and information theoretic foundations, respectively. Numerosity reduction methods can be divided into methods based on variance, geometry, and isolation. For dimensionality reduction, under the spectral category, I propose weighted Fisher discriminant analysis, Roweis discriminant analysis, and image quality aware embedding. I also propose quantile-quantile embedding as a probabilistic method where the distribution of embedding is chosen by the user. Backprojection, Fisher losses, and dynamic triplet sampling using Bayesian updating are other proposed methods in the neural network-based category. Backprojection is for training shallow networks with a projection-based perspective in manifold learning. Two Fisher losses are proposed for training Siamese triplet networks for increasing and decreasing the inter- and intra-class variances, respectively. Two dynamic triplet mining methods, which are based on Bayesian updating to draw triplet samples stochastically, are proposed. For numerosity reduction, principal sample analysis and instance ranking by matrix decomposition are the proposed variance-based methods; these methods rank instances using inter-/intra-class variances and matrix factorization, respectively. Curvature anomaly detection, in which the points are assumed to be the vertices of polyhedron, and isolation Mondrian forest are the proposed methods based on geometry and isolation, respectively. To assess the proposed tools developed for data reduction, I apply them to some applications in medical image analysis, image processing, and computer vision. Data reduction, used as a pre-processing tool, has different applications because it provides various ways of feature extraction and prototype selection for applying to different types of data. Dimensionality reduction extracts informative features and prototype selection selects the most informative data instances. For example, for medical image analysis, I use Fisher losses and dynamic triplet sampling for embedding histopathology image patches and demonstrating how different the tumorous cancer tissue types are from the normal ones. I also propose offline/online triplet mining using extreme distances for this embedding. In image processing and computer vision application, I propose Roweisfaces and Roweisposes for face recognition and 3D action recognition, respectively, using my proposed Roweis discriminant analysis method. I also introduce the concepts of anomaly landscape and anomaly path using the proposed curvature anomaly detection and use them to denoise images and video frames. I report extensive experiments, on different datasets, to show the effectiveness of the proposed algorithms. By experiments, I demonstrate that the proposed methods are useful for extracting informative features and instances for better accuracy, representation, prediction, class separation, data reduction, and embedding. I show that the proposed dimensionality reduction methods can extract informative features for better separation of classes. An example is obtaining an embedding space for separating cancer histopathology patches from the normal patches which helps hospitals diagnose cancers more easily in an automatic way. I also show that the proposed numerosity reduction methods are useful for ranking data instances based on their importance and reducing data volumes without a significant drop in performance of machine learning and data science algorithms

Facial Expression Analysis under Partial Occlusion: A Survey

Automatic machine-based Facial Expression Analysis (FEA) has made substantial progress in the past few decades driven by its importance for applications in psychology, security, health, entertainment and human computer interaction. The vast majority of completed FEA studies are based on non-occluded faces collected in a controlled laboratory environment. Automatic expression recognition tolerant to partial occlusion remains less understood, particularly in real-world scenarios. In recent years, efforts investigating techniques to handle partial occlusion for FEA have seen an increase. The context is right for a comprehensive perspective of these developments and the state of the art from this perspective. This survey provides such a comprehensive review of recent advances in dataset creation, algorithm development, and investigations of the effects of occlusion critical for robust performance in FEA systems. It outlines existing challenges in overcoming partial occlusion and discusses possible opportunities in advancing the technology. To the best of our knowledge, it is the first FEA survey dedicated to occlusion and aimed at promoting better informed and benchmarked future work.Comment: Authors pre-print of the article accepted for publication in ACM Computing Surveys (accepted on 02-Nov-2017