99 research outputs found

    Facial Landmark Detection Evaluation on MOBIO Database

    Full text link
    MOBIO is a bi-modal database that was captured almost exclusively on mobile phones. It aims to improve research into deploying biometric techniques to mobile devices. Research has been shown that face and speaker recognition can be performed in a mobile environment. Facial landmark localization aims at finding the coordinates of a set of pre-defined key points for 2D face images. A facial landmark usually has specific semantic meaning, e.g. nose tip or eye centre, which provides rich geometric information for other face analysis tasks such as face recognition, emotion estimation and 3D face reconstruction. Pretty much facial landmark detection methods adopt still face databases, such as 300W, AFW, AFLW, or COFW, for evaluation, but seldomly use mobile data. Our work is first to perform facial landmark detection evaluation on the mobile still data, i.e., face images from MOBIO database. About 20,600 face images have been extracted from this audio-visual database and manually labeled with 22 landmarks as the groundtruth. Several state-of-the-art facial landmark detection methods are adopted to evaluate their performance on these data. The result shows that the data from MOBIO database is pretty challenging. This database can be a new challenging one for facial landmark detection evaluation.Comment: 13 pages, 10 figure

    Optimising multimodal fusion for biometric identification systems

    Get PDF
    Biometric systems are automatic means for imitating the human brain’s ability of identifying and verifying other humans by their behavioural and physiological characteristics. A system, which uses more than one biometric modality at the same time, is known as a multimodal system. Multimodal biometric systems consolidate the evidence presented by multiple biometric sources and typically provide better recognition performance compared to systems based on a single biometric modality. This thesis addresses some issues related to the implementation of multimodal biometric identity verification systems. The thesis assesses the feasibility of using commercial offthe-shelf products to construct deployable multimodal biometric system. It also identifies multimodal biometric fusion as a challenging optimisation problem when one considers the presence of several configurations and settings, in particular the verification thresholds adopted by each biometric device and the decision fusion algorithm implemented for a particular configuration. The thesis proposes a novel approach for the optimisation of multimodal biometric systems based on the use of genetic algorithms for solving some of the problems associated with the different settings. The proposed optimisation method also addresses some of the problems associated with score normalization. In addition, the thesis presents an analysis of the performance of different fusion rules when characterising the system users as sheep, goats, lambs and wolves. The results presented indicate that the proposed optimisation method can be used to solve the problems associated with threshold settings. This clearly demonstrates a valuable potential strategy that can be used to set a priori thresholds of the different biometric devices before using them. The proposed optimisation architecture addressed the problem of score normalisation, which makes it an effective “plug-and-play” design philosophy to system implementation. The results also indicate that the optimisation approach can be used for effectively determining the weight settings, which is used in many applications for varying the relative importance of the different performance parameters

    Detección rápida de puntos de referencia faciales y aplicaciones: estudio de la bibliografía

    Get PDF
    Dense facial landmark detection is one of the key elements of face processing pipeline. It is used in virtual face reenactment, emotion recognition, driver status tracking, etc. Early approaches were suitable for facial landmark detection in controlled environments only, which is clearly insufficient. Neural networks have shown an astonishing qualitative improvement for in-the-wild face landmark detection problem, and are now being studied by many researchers in the field. Numerous bright ideas are proposed, often complimentary to each other. However, exploration of the whole volume of novel approaches is quite challenging. Therefore, we present this survey, where we summarize state-of-the-art algorithms into categories, provide a comparison of recently introduced in-the-wild datasets (e.g., 300W, AFLW, COFW, WFLW) that contain images with large pose, face occlusion, taken in unconstrained conditions. In addition to quality, applications require fast inference, and preferably on mobile devices. Hence, we include information about algorithm inference speed both on desktop and mobile hardware, which is rarely studied. Importantly, we highlight problems of algorithms, their applications, vulnerabilities, and briefly touch on established methods. We hope that the reader will find many novel ideas, will see how the algorithms are used in applications, which will enable further research.La detección de puntos de referenda faciales densos es uno de los elementos clave del proceso de procesamiento de rostros. Se utiliza en la anünación de rostros virtuales, el reconocüniento de emociones, el seguimiento del estado del conductor, etc. Los prüneros enfoques eran adecuados para la detección de puntos de referencia faciales solo en entornos controlados, lo que claramente es insuficiente. Las redes neuronales han mostrado una asombrosa mejora cualitativa para el problema de detección de puntos de referencia faciales en condiciones del mundo real, y ahora están siendo estudiadas por muchos investigadores en el campo. Se proponen numerosas ideas brillantes, a menudo complementarias. Sin embargo, la exploración de todo el volumen de enfoques novedosos es bastante desafiante. Por lo tanto, presentamos esta encuesta, donde resumimos los algoritmos de última generación en categorías, brindamos una comparación de los conjuntos de datos introducidos recientemente (por ejemplo, 300W, AFLW, COFW, WFLW) que contienen imágenes con pose grande, oclusión facial, tomadas en condiciones sin restricciones. Además de calidad, las aplicaciones requieren una inferencia rápida y preferentemente en dispositivos móviles. Por lo tanto, incluimos información sobre la velocidad de inferencia de algoritmos tanto en hardware de escritorio como móvil, que rara vez se estudia. Es importante destacar que destacamos los problemas de los algoritmos, sus aplicaciones, vulnerabilidades y mencionamos brevemente los métodos establecidos. Esperamos que el lector encuentre muchas ideas novedosas, vea cómo se utilizan los algoritmos en las aplicaciones, lo que permitirá futuras investigaciones.Facultad de Informátic

    Advances in generative modelling: from component analysis to generative adversarial networks

    Get PDF
    This Thesis revolves around datasets and algorithms, with a focus on generative modelling. In particular, we first turn our attention to a novel, multi-attribute, 2D facial dataset. We then present deterministic as well as probabilistic Component Analysis (CA) techniques which can be applied to multi-attribute 2D as well as 3D data. We finally present deep learning generative approaches specially designed to manipulate 3D facial data. Most 2D facial datasets that are available in the literature, are: a) automatically or semi-automatically collected and thus contain noisy labels, hindering the benchmarking and comparisons between algorithms. Moreover, they are not annotated for multiple attributes. In the first part of the Thesis, we present the first manually collected and annotated database, which contains labels for multiple attributes. As we demonstrate in a series of experiments, it can be used in a number of applications ranging from image translation to age-invariant face recognition. Moving on, we turn our attention to CA methodologies. CA approaches, although being able to only capture linear relationships between data, can still be proven to be efficient in data such as UV maps or 3D data registered in a common template, since they are well aligned. The introduction of more complex datasets in the literature, which contain labels for multiple attributes, naturally brought the need for novel algorithms that can simultaneously handle multiple attributes. In this Thesis, we cover novel CA approaches which are specifically designed to be utilised in datasets annotated with respect to multiple attributes and can be used in a variety of tasks, such as 2D image denoising and translation, as well as 3D data generation and identification. Nevertheless, while CA methods are indeed efficient when handling registered 3D facial data, linear 3D generative models lack details when it comes to reconstructing or generating finer facial characteristics. To alleviate this, in the final part of this Thesis we propose a novel generative framework harnessing the power of Generative Adversarial Networks.Open Acces

    The Multiscenario Multienvironment BioSecure Multimodal Database (BMDB)

    Get PDF
    A new multimodal biometric database designed and acquired within the framework of the European BioSecure Network of Excellence is presented. It is comprised of more than 600 individuals acquired simultaneously in three scenarios: 1) over the Internet, 2) in an office environment with desktop PC, and 3) in indoor/outdoor environments with mobile portable hardware. The three scenarios include a common part of audio/video data. Also, signature and fingerprint data have been acquired both with desktop PC and mobile portable hardware. Additionally, hand and iris data were acquired in the second scenario using desktop PC. Acquisition has been conducted by 11 European institutions. Additional features of the BioSecure Multimodal Database (BMDB) are: two acquisition sessions, several sensors in certain modalities, balanced gender and age distributions, multimodal realistic scenarios with simple and quick tasks per modality, cross-European diversity, availability of demographic data, and compatibility with other multimodal databases. The novel acquisition conditions of the BMDB allow us to perform new challenging research and evaluation of either monomodal or multimodal biometric systems, as in the recent BioSecure Multimodal Evaluation campaign. A description of this campaign including baseline results of individual modalities from the new database is also given. The database is expected to be available for research purposes through the BioSecure Association during 2008Comment: Published at IEEE Transactions on Pattern Analysis and Machine Intelligence journa

    AnchorFace: An Anchor-based Facial Landmark Detector Across Large Poses

    Full text link
    Facial landmark localization aims to detect the predefined points of human faces, and the topic has been rapidly improved with the recent development of neural network based methods. However, it remains a challenging task when dealing with faces in unconstrained scenarios, especially with large pose variations. In this paper, we target the problem of facial landmark localization across large poses and address this task based on a split-and-aggregate strategy. To split the search space, we propose a set of anchor templates as references for regression, which well addresses the large variations of face poses. Based on the prediction of each anchor template, we propose to aggregate the results, which can reduce the landmark uncertainty due to the large poses. Overall, our proposed approach, named AnchorFace, obtains state-of-the-art results with extremely efficient inference speed on four challenging benchmarks, i.e. AFLW, 300W, Menpo, and WFLW dataset. Code will be available at https://github.com/nothingelse92/AnchorFace.Comment: To appear in AAAI 202

    Towards Engineering Reliable Keystroke Biometrics Systems

    Get PDF
    In this thesis, we argue that most of the work in the literature on behavioural-based biometric systems using AI and machine learning is immature and unreliable. Our analysis and experimental results show that designing reliable behavioural-based biometric systems requires a systematic and complicated process. We first discuss the limitation in existing work and the use of conventional machine learning methods. We use the biometric zoos theory to demonstrate the challenge of designing reliable behavioural-based biometric systems. Then, we outline the common problems in engineering reliable biometric systems. In particular, we focus on the need for novelty detection machine learning models and adaptive machine learning algorithms. We provide a systematic approach to design and build reliable behavioural-based biometric systems. In our study, we apply the proposed approach to keystroke dynamics. Keystroke dynamics is behavioural-based biometric that identify individuals by measuring their unique typing behaviours on physical or soft keyboards. Our study shows that it is possible to design reliable behavioral-based biometrics and address the gaps in the literature
    corecore