Tesi sul tema "Reconnaissance faciale (Informatique)"
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Kose, Neslihan. "Leurrage et dissimulation en reconnaissance faciale : analyses et contre attaques". Electronic Thesis or Diss., Paris, ENST, 2014. http://www.theses.fr/2014ENST0020.
Testo completoHuman recognition has become an important topic as the need and investments for security applications grow continuously. Numerous biometric systems exist which utilize various human characteristics. Among all biometrics traits, face recognition is advantageous in terms of accessibility and reliability. In the thesis, two challenges in face recognition are analyzed. The first one is face spoofing. Spoofing in face recognition is explained together with the countermeasure techniques that are proposed for the protection of face recognition systems against spoofing attacks. For this purpose, both 2D photograph and 3D mask attacks are analyzed. The second challenge explored in the thesis is disguise variations, which are due to facial alterations, facial makeup and facial accessories (occlusions). The impact of these disguise variations on face recognition is explored, separately. Then, techniques which are robust against disguise variations are proposed
Kose, Neslihan. "Leurrage et dissimulation en reconnaissance faciale : analyses et contre attaques". Thesis, Paris, ENST, 2014. http://www.theses.fr/2014ENST0020/document.
Testo completoHuman recognition has become an important topic as the need and investments for security applications grow continuously. Numerous biometric systems exist which utilize various human characteristics. Among all biometrics traits, face recognition is advantageous in terms of accessibility and reliability. In the thesis, two challenges in face recognition are analyzed. The first one is face spoofing. Spoofing in face recognition is explained together with the countermeasure techniques that are proposed for the protection of face recognition systems against spoofing attacks. For this purpose, both 2D photograph and 3D mask attacks are analyzed. The second challenge explored in the thesis is disguise variations, which are due to facial alterations, facial makeup and facial accessories (occlusions). The impact of these disguise variations on face recognition is explored, separately. Then, techniques which are robust against disguise variations are proposed
Abdat, Faiza. "Reconnaissance automatique des émotions par données multimodales : expressions faciales et des signaux physiologiques". Electronic Thesis or Diss., Metz, 2010. http://www.theses.fr/2010METZ035S.
Testo completoThis thesis presents a generic method for automatic recognition of emotions from a bimodal system based on facial expressions and physiological signals. This data processing approach leads to better extraction of information and is more reliable than single modality. The proposed algorithm for facial expression recognition is based on the distance variation of facial muscles from the neutral state and on the classification by means of Support Vector Machines (SVM). And the emotion recognition from physiological signals is based on the classification of statistical parameters by the same classifier. In order to have a more reliable recognition system, we have combined the facial expressions and physiological signals. The direct combination of such information is not trivial giving the differences of characteristics (such as frequency, amplitude, variation, and dimensionality). To remedy this, we have merged the information at different levels of implementation. At feature-level fusion, we have tested the mutual information approach for selecting the most relevant and principal component analysis to reduce their dimensionality. For decision-level fusion we have implemented two methods; the first based on voting process and another based on dynamic Bayesian networks. The optimal results were obtained with the fusion of features based on Principal Component Analysis. These methods have been tested on a database developed in our laboratory from healthy subjects and inducing with IAPS pictures. A self-assessment step has been applied to all subjects in order to improve the annotation of images used for induction. The obtained results have shown good performance even in presence of variability among individuals and the emotional state variability for several days
Abdat, Faiza. "Reconnaissance automatique des émotions par données multimodales : expressions faciales et des signaux physiologiques". Thesis, Metz, 2010. http://www.theses.fr/2010METZ035S/document.
Testo completoThis thesis presents a generic method for automatic recognition of emotions from a bimodal system based on facial expressions and physiological signals. This data processing approach leads to better extraction of information and is more reliable than single modality. The proposed algorithm for facial expression recognition is based on the distance variation of facial muscles from the neutral state and on the classification by means of Support Vector Machines (SVM). And the emotion recognition from physiological signals is based on the classification of statistical parameters by the same classifier. In order to have a more reliable recognition system, we have combined the facial expressions and physiological signals. The direct combination of such information is not trivial giving the differences of characteristics (such as frequency, amplitude, variation, and dimensionality). To remedy this, we have merged the information at different levels of implementation. At feature-level fusion, we have tested the mutual information approach for selecting the most relevant and principal component analysis to reduce their dimensionality. For decision-level fusion we have implemented two methods; the first based on voting process and another based on dynamic Bayesian networks. The optimal results were obtained with the fusion of features based on Principal Component Analysis. These methods have been tested on a database developed in our laboratory from healthy subjects and inducing with IAPS pictures. A self-assessment step has been applied to all subjects in order to improve the annotation of images used for induction. The obtained results have shown good performance even in presence of variability among individuals and the emotional state variability for several days
Le, Meur Julien. "Conception, assemblage, optimisation et test de modules intégrés d'illumination structurée à base d'éléments optiques diffractifs : application particulière à la reconnaissance faciale". Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2018. http://www.theses.fr/2018IMTA0121.
Testo completoThis thesis work aimed to design, assemble, optimize and test structured illumination modules based on diffractive optical elements (DOEs) for facial recognition application on mobile devices (smartphones, tablets). The integration of modules into smartphones involved significant constraints in terms of miniaturization, energy consumption, cost and laser safety. The key element of each module was a Fourier DOE with a diffraction angle greater than the limit of the paraxial scalar diffraction model to illuminate the surface of a face at a distance of an arm reach. In order to facilitate the design (relaxation of angular constraints), manufacturing (minimization of the zero order diffraction efficiency) and replication of DOEs, the first axis of research consisted in designing and manufacturing hybrid "angle enlarger" devices combining DOEs and conventional divergent optics. The second part concerned the design of the DOEs, which had to take into account both the parameters of the low-cost illumination and image acquisition systems used, in particular to control the presence of laser speckle on the desired diffraction pattern (control imposed by the facial recognition and fraud detection algorithms used). The know-how acquired in the field of structured illumination generated by DOEs has been extended and transposed to three other applications in the fields of vibrometry, civil and commercial aviation, and military aviation
Deramgozin, Mohammadmahdi. "Développement de modèles de reconnaissance des expressions faciales à base d’apprentissage profond pour les applications embarquées". Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0286.
Testo completoThe field of Facial Emotion Recognition (FER) is pivotal in advancing human-machine interactions and finds essential applications in healthcare for conditions like depression and anxiety. Leveraging Convolutional Neural Networks (CNNs), this thesis presents a progression of models aimed at optimizing emotion detection and interpretation. The initial model is resource-frugal but competes favorably with state-of-the-art solutions, making it a strong candidate for embedded systems constrained in computational and memory resources. To capture the complexity and ambiguity of human emotions, the research work presented in this thesis enhances this CNN-based foundational model by incorporating facial Action Units (AUs). This approach not only refines emotion detection but also provides interpretability by identifying specific AUs tied to each emotion. Further sophistication is achieved by introducing neural attention mechanisms—both spatial and channel-based—improving the model's focus on salient facial features. This makes the CNN-based model adapted well to real-world scenarios, such as partially obscured or subtle facial expressions. Based on the previous results, in this thesis we propose finally an optimized, yet computationally efficient, CNN model that is ideal for resource-limited environments like embedded systems. While it provides a robust solution for FER, this research also identifies perspectives for future work, such as real-time applications and advanced techniques for model interpretability
Ballihi, Lahoucine. "Biométrie faciale 3D par apprentissage des caractéristiques géométriques : Application à la reconnaissance des visages et à la classification du genre". Phd thesis, Université des Sciences et Technologie de Lille - Lille I, 2012. http://tel.archives-ouvertes.fr/tel-00726299.
Testo completoAl, chanti Dawood. "Analyse Automatique des Macro et Micro Expressions Faciales : Détection et Reconnaissance par Machine Learning". Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAT058.
Testo completoFacial expression analysis is an important problem in many biometric tasks, such as face recognition, face animation, affective computing and human computer interface. In this thesis, we aim at analyzing facial expressions of a face using images and video sequences. We divided the problem into three leading parts.First, we study Macro Facial Expressions for Emotion Recognition and we propose three different levels of feature representations. Low-level feature through a Bag of Visual Word model, mid-level feature through Sparse Representation and hierarchical features through a Deep Learning based method. The objective of doing this is to find the most effective and efficient representation that contains distinctive information of expressions and that overcomes various challenges coming from: 1) intrinsic factors such as appearance and expressiveness variability and 2) extrinsic factors such as illumination, pose, scale and imaging parameters, e.g., resolution, focus, imaging, noise. Then, we incorporate the time dimension to extract spatio-temporal features with the objective to describe subtle feature deformations to discriminate ambiguous classes.Second, we direct our research toward transfer learning, where we aim at Adapting Facial Expression Category Models to New Domains and Tasks. Thus we study domain adaptation and zero shot learning for developing a method that solves the two tasks jointly. Our method is suitable for unlabelled target datasets coming from different data distributions than the source domain and for unlabelled target datasets with different label distributions but sharing the same context as the source domain. Therefore, to permit knowledge transfer between domains and tasks, we use Euclidean learning and Convolutional Neural Networks to design a mapping function that map the visual information coming from facial expressions into a semantic space coming from a Natural Language model that encodes the visual attribute description or use the label information. The consistency between the two subspaces is maximized by aligning them using the visual feature distribution.Third, we study Micro Facial Expression Detection. We propose an algorithm to spot micro-expression segments including the onset and offset frames and to spatially pinpoint in each image space the regions involved in the micro-facial muscle movements. The problem is formulated into Anomaly Detection due to the fact that micro-expressions occur infrequently and thus leading to few data generation compared to natural facial behaviours. In this manner, first, we propose a deep Recurrent Convolutional Auto-Encoder to capture spatial and motion feature changes of natural facial behaviours. Then, a statistical based model for estimating the probability density function of normal facial behaviours while associating a discriminating score to spot micro-expressions is learned based on a Gaussian Mixture Model. Finally, an adaptive thresholding technique for identifying micro expressions from natural facial behaviour is proposed.Our algorithms are tested over deliberate and spontaneous facial expression benchmarks
Huang, Di. "Robust face recognition based on three dimensional data". Phd thesis, Ecole Centrale de Lyon, 2011. http://tel.archives-ouvertes.fr/tel-00693158.
Testo completoBen, Soltana Wael. "Optimisation de stratégies de fusion pour la reconnaissance de visages 3D". Phd thesis, Ecole Centrale de Lyon, 2012. http://tel.archives-ouvertes.fr/tel-01070638.
Testo completoLemaire, Pierre. "Contributions à l'analyse de visages en 3D : approche régions, approche holistique et étude de dégradations". Phd thesis, Ecole Centrale de Lyon, 2013. http://tel.archives-ouvertes.fr/tel-01002114.
Testo completoZhao, Xi. "3D face analysis : landmarking, expression recognition and beyond". Phd thesis, Ecole Centrale de Lyon, 2010. http://tel.archives-ouvertes.fr/tel-00599660.
Testo completoPeyrard, Clément. "Single image super-resolution based on neural networks for text and face recognition". Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEI083/document.
Testo completoThis thesis is focussed on super-resolution (SR) methods for improving automatic recognition system (Optical Character Recognition, face recognition) in realistic contexts. SR methods allow to generate high resolution images from low resolution ones. Unlike upsampling methods such as interpolation, they restore spatial high frequencies and compensate artefacts such as blur or jaggy edges. In particular, example-based approaches learn and model the relationship between low and high resolution spaces via pairs of low and high resolution images. Artificial Neural Networks are among the most efficient systems to address this problem. This work demonstrate the interest of SR methods based on neural networks for improved automatic recognition systems. By adapting the data, it is possible to train such Machine Learning algorithms to produce high-resolution images. Convolutional Neural Networks are especially efficient as they are trained to simultaneously extract relevant non-linear features while learning the mapping between low and high resolution spaces. On document text images, the proposed method improves OCR accuracy by +7.85 points compared with simple interpolation. The creation of an annotated image dataset and the organisation of an international competition (ICDAR2015) highlighted the interest and the relevance of such approaches. Moreover, if a priori knowledge is available, it can be used by a suitable network architecture. For facial images, face features are critical for automatic recognition. A two step method is proposed in which image resolution is first improved, followed by specialised models that focus on the essential features. An off-the-shelf face verification system has its performance improved from +6.91 up to +8.15 points. Finally, to address the variability of real-world low-resolution images, deep neural networks allow to absorb the diversity of the blurring kernels that characterise the low-resolution images. With a single model, high-resolution images are produced with natural image statistics, without any knowledge of the actual observation model of the low-resolution image
Baccouche, Moez. "Apprentissage neuronal de caractéristiques spatio-temporelles pour la classification automatique de séquences vidéo". Phd thesis, INSA de Lyon, 2013. http://tel.archives-ouvertes.fr/tel-00932662.
Testo completoDagnes, Nicole. "3D human face analysis for recognition applications and motion capture". Thesis, Compiègne, 2020. http://www.theses.fr/2020COMP2542.
Testo completoThis thesis is intended as a geometrical study of the three-dimensional facial surface, whose aim is to provide an application framework of entities coming from Differential Geometry context to use as facial descriptors in face analysis applications, like FR and FER fields. Indeed, although every visage is unique, all faces are similar and their morphological features are the same for all mankind. Hence, it is primary for face analysis to extract suitable features. All the facial features, proposed in this study, are based only on the geometrical properties of the facial surface. Then, these geometrical descriptors and the related entities proposed have been applied in the description of facial surface in pattern recognition contexts. Indeed, the final goal of this research is to prove that Differential Geometry is a comprehensive tool oriented to face analysis and geometrical features are suitable to describe and compare faces and, generally, to extract relevant information for human face analysis in different practical application fields. Finally, since in the last decades face analysis has gained great attention also for clinical application, this work focuses on musculoskeletal disorders analysis by proposing an objective quantification of facial movements for helping maxillofacial surgery and facial motion rehabilitation. At this time, different methods are employed for evaluating facial muscles function. This research work investigates the 3D motion capture system, adopting the Technology, Sport and Health platform, located in the Innovation Centre of the University of Technology of Compiègne, in the Biomechanics and Bioengineering Laboratory (BMBI)
Zhang, Wuming. "Towards non-conventional face recognition : shadow removal and heterogeneous scenario". Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEC030/document.
Testo completoIn recent years, biometrics have received substantial attention due to the evergrowing need for automatic individual authentication. Among various physiological biometric traits, face offers unmatched advantages over the others, such as fingerprints and iris, because it is natural, non-intrusive and easily understandable by humans. Nowadays conventional face recognition techniques have attained quasi-perfect performance in a highly constrained environment wherein poses, illuminations, expressions and other sources of variations are strictly controlled. However these approaches are always confined to restricted application fields because non-ideal imaging environments are frequently encountered in practical cases. To adaptively address these challenges, this dissertation focuses on this unconstrained face recognition problem, where face images exhibit more variability in illumination. Moreover, another major question is how to leverage limited 3D shape information to jointly work with 2D based techniques in a heterogeneous face recognition system. To deal with the problem of varying illuminations, we explicitly build the underlying reflectance model which characterizes interactions between skin surface, lighting source and camera sensor, and elaborate the formation of face color. With this physics-based image formation model involved, an illumination-robust representation, namely Chromaticity Invariant Image (CII), is proposed which can subsequently help reconstruct shadow-free and photo-realistic color face images. Due to the fact that this shadow removal process is achieved in color space, this approach could thus be combined with existing gray-scale level lighting normalization techniques to further improve face recognition performance. The experimental results on two benchmark databases, CMU-PIE and FRGC Ver2.0, demonstrate the generalization ability and robustness of our approach to lighting variations. We further explore the effective and creative use of 3D data in heterogeneous face recognition. In such a scenario, 3D face is merely available in the gallery set and not in the probe set, which one would encounter in real-world applications. Two Convolutional Neural Networks (CNN) are constructed for this purpose. The first CNN is trained to extract discriminative features of 2D/3D face images for direct heterogeneous comparison, while the second CNN combines an encoder-decoder structure, namely U-Net, and Conditional Generative Adversarial Network (CGAN) to reconstruct depth face image from its counterpart in 2D. Specifically, the recovered depth face images can be fed to the first CNN as well for 3D face recognition, leading to a fusion scheme which achieves gains in recognition performance. We have evaluated our approach extensively on the challenging FRGC 2D/3D benchmark database. The proposed method compares favorably to the state-of-the-art and show significant improvement with the fusion scheme
Ji, Yi. "Object classification in images and videos : Application to facial expressions". Lyon, INSA, 2010. http://theses.insa-lyon.fr/publication/2010ISAL0107/these.pdf.
Testo completoDans cette thèse, nous avons abordé la problématique de la classification d'objets puis nous l'avons appliqué à la classification et la reconnaissance des expressions faciales. D'abord, nous nous sommes inspirés des processus de Dirichlet, comme des distributions dans l'espace des distributions, qui génèrent des composantes intermédiaires permettant d'améliorer la catégorisation d'objets. Ce modèle, utilisé notamment dans la classification sémantique de documents, se caractérise par le fait d'être non paramétrique, et d'être hiérarchique. Dans une première phase, l'ensemble des composantes intermédiaires de base sont extraites en utilisant l'apprentissage bayésien par MCMC puis une sélection itérative des classifiers faibles les plus distinctifs parmi toutes les composantes est opéré par Adaboost. Notre objectif est de cerner les distributions des composantes latentes aussi bien celles partagées par les différentes classes que celles associées à une catégorie particulière. Nous avons cherché dans cette seconde partie à appliquer notre approche de classification aux expressions faciales. Ce travail a consisté à trouver les méthodes adéquates pour décrire les aspects statiques et dynamiques au cours de l'expression faciale, et donc à concevoir de nouveaux descripteurs capables de représenter les caractéristiques des mouvements des muscles faciaux, et par là même, identifier la catégorie de l'expression
Li, Huibin. "Towards three-dimensional face recognition in the real". Phd thesis, Ecole Centrale de Lyon, 2013. http://tel.archives-ouvertes.fr/tel-00998798.
Testo completoMainsant, Marion. "Apprentissage continu sous divers scénarios d'arrivée de données : vers des applications robustes et éthiques de l'apprentissage profond". Electronic Thesis or Diss., Université Grenoble Alpes, 2023. http://www.theses.fr/2023GRALS045.
Testo completoThe human brain continuously receives information from external stimuli. It then has the ability to adapt to new knowledge while retaining past events. Nowadays, more and more artificial intelligence algorithms aim to learn knowledge in the same way as a human being. They therefore have to be able to adapt to a large variety of data arriving sequentially and available over a limited period of time. However, when a deep learning algorithm learns new data, the knowledge contained in the neural network overlaps old one and the majority of the past information is lost, a phenomenon referred in the literature as catastrophic forgetting. Numerous methods have been proposed to overcome this issue, but as they were focused on providing the best performance, studies have moved away from real-life applications where algorithms need to adapt to changing environments and perform, no matter the type of data arrival. In addition, most of the best state of the art methods are replay methods which retain a small memory of the past and consequently do not preserve data privacy.In this thesis, we propose to explore data arrival scenarios existing in the literature, with the aim of applying them to facial emotion recognition, which is essential for human-robot interactions. To this end, we present Dream Net - Data-Free, a privacy preserving algorithm, able to adapt to a large number of data arrival scenarios without storing any past samples. After demonstrating the robustness of this algorithm compared to existing state-of-the-art methods on standard computer vision databases (Mnist, Cifar-10, Cifar-100 and Imagenet-100), we show that it can also adapt to more complex facial emotion recognition databases. We then propose to embed the algorithm on a Nvidia Jetson nano card creating a demonstrator able to learn and predict emotions in real-time. Finally, we discuss the relevance of our approach for bias mitigation in artificial intelligence, opening up perspectives towards a more ethical AI
Dubuisson, Séverine. "Analyses d'expressions faciales". Compiègne, 2001. http://www.theses.fr/2001COMP1360.
Testo completoBen, Amor Boulbaba. "Contributions à la modélisation et à la reconnaissance faciales 3D". Ecully, Ecole centrale de Lyon, 2006. http://bibli.ec-lyon.fr/exl-doc/bbenamor.pdf.
Testo completoNowadays, face recognition represent one of the privileged fields of search due to the emergence of the security in many domains. This thesis lies within this scope, and more particularly, in the three-dimensional face recognition. In this work, we are interested to the complementary fields : 3D face modelling and recognition. Whereas modelling task aims at 3D face shape acquisition, recognition task aims at the identification of a probe face model among faces stored in a data base (gallery) or verify his identity. For that, two approaches are studied and implemented : (i) an hybrid approach for facial acquisition based on active vision and geometrical modelling, and (ii) an approach for aligning facial surfaces before computing similarities between 3D models. A new 3D face database is collected within the IV 2 French project in order to make signifiant experiments and evaluations of the developed algorithms
Allaert, Benjamin. "Analyse des expressions faciales dans un flux vidéo". Thesis, Lille 1, 2018. http://www.theses.fr/2018LIL1I021/document.
Testo completoFacial expression recognition has attracted great interest over the past decade in wide application areas, such as human behavior analysis, e-health and marketing. In this thesis we explore a new approach to step forward towards in-the-wild expression recognition. Special attention has been paid to encode respectively small/large facial expression amplitudes, and to analyze facial expressions in presence of varying head pose. The first challenge addressed concerns varying facial expression amplitudes. We propose an innovative motion descriptor called LMP. This descriptor takes into account mechanical facial skin deformation properties. When extracting motion information from the face, the unified approach deals with inconsistencies and noise, caused by face characteristics. The main originality of our approach is a unified approach for both micro and macro expression recognition, with the same facial recognition framework. The second challenge addressed concerns important head pose variations. In facial expression analysis, the face registration step must ensure that minimal deformation appears. Registration techniques must be used with care in presence of unconstrained head pose as facial texture transformations apply. Hence, it is valuable to estimate the impact of alignment-related induced noise on the global recognition performance. For this, we propose a new database, called SNaP-2DFe, allowing to study the impact of head pose and intra-facial occlusions on expression recognition approaches. We prove that the usage of face registration approach does not seem adequate for preserving the features encoding facial expression deformations
Matta, Federico. "Video person recognition strategies using head motion and facial appearance". Nice, 2008. http://www.theses.fr/2008NICE4038.
Testo completoDans cette thèse, nous avons principalement exploré l'utilisation de l'information temporelle des séquences vidéo afin de l'appliquer à la reconnaissance de personne et de son genre; en particulier, nous nous concentrons sur l'analyse du mouvement de la tête et du visage ainsi que sur leurs applications potentielles comme éléments d'identification biométriques. De plus, nous cherchons à exploiter la majorité de l'information contenue dans la vidéo pour la reconnaissance automatique; plus précisément, nous regardons la possibilité d'intégrer dans un système biométrique multimodal l'information liée au mouvement de la tête et de la bouche avec celle de l'aspect du visage, et nous étudions l'extraction des nouveaux paramètres spatio-temporels pour la reconnaissance faciale. Nous présentons d'abord un système de reconnaissance de la personne qui exploite l'information relative au mouvement spontané de la tête. Cette information est extraite par le suivi dans le plan image de certains éléments caractéristiques du visage. En particulier, nous détaillons la façon dont dans chaque séquence vidéo le visage est tout d'abord détecté semi-automatiquement, puis le suivi automatique dans le temps de certains éléments caractéristiques en utilisant une approche basée sur l'appariement de bloques (template matching). Ensuite, nous exposons les normalisations géométriques des signaux que nous avons obtenus, le calcul des vecteurs caractéristiques, et la façon dont ils sont utilisés pour estimer les modèles des clients, approximés avec des modèles de mélange de gaussiennes. En fin de compte, nous parvenons à identifier et vérifier l'identité de la personne en appliquant la théorie des probabilités et la règle de décision bayésienne (aussi appelée inférence bayésienne). Nous proposons ensuite une extension multimodale de notre système de reconnaissance de la personne; plus précisément, nous intégrons à travers un cadre probabiliste unifié l'information sur le mouvement de la tête avec celles liées au mouvement de la bouche et à l'aspect du visage. En fait nous développons un nouveau sous-système temporel qui a un espace caractéristique étendu, lequel est enrichi par certains paramètres supplémentaires relatif au mouvement de la bouche; dans le même temps nous introduisons un sous-système spatial complémentaire au précédent, basé sur une extension probabiliste de l'approche Eigenfaces d'origine. Ensuite, une étape d'intégration combine les scores de similarité des deux sous-systèmes parallèles, grâce à une stratégie appropriée de fusion d'opinions. Enfin nous étudions une méthode pratique pour extraire de nouveaux paramètres spatio-temporels liés au visage à partir des séquences vidéo; le but est de distinguer l'identité et le genre de la personne. À cette fin nous développons un système de reconnaissance appelé tomovisages (tomofaces), qui applique la technique de la tomographie vidéo pour résumer en une seule image l'information relative au mouvement et à l'aspect du visage d'une personne. Puis, nous détaillons la projection linéaire à partir de l'espace de l'image en rayons X à un espace caractéristique de dimension réduite, l'estimation des modèles des utilisateurs en calculant les représentants des clusters correspondants, et la reconnaissance de l'identité et du genre par le biais d'un classificateur de plus proche voisin, qui adopte des distances dans le sous-espace
In questa tesi di dottorato esploriamo la possibilità di riconoscere l'identità e il sesso di una persona attraverso l'utilizzo dell'informazione temporale disponibile in alcune sequenze video, in particolare ci concentriamo sull'analisi del movimento della testa e del viso, nonché del loro potenziale utilizzo come identificatiori biometrici. Esaminiamo inoltre la problematica relativa al fatto di sfruttare la maggior parte dell'informazione presente nei video per effettuare il riconoscimento automatico della persona; più precisamente, analizziamo la possibilità di integrare in un sistema biometrico multimodale l'informazione relativa al movimento della testa e della bocca con quella dell'aspetto del viso, e studiamo il calcolo di nuovi parametri spazio-temporali che siano utilizzabili per il riconoscimento stesso. In primo luogo presentiamo un sistema di riconoscimento biometrico della persona che sfrutti l'informazione legata al movimento naturale della testa, il quale è estratto seguendo la posizione nel piano immagine di alcuni elementi caratteristici del viso. In particolare descriviamo come in una sequenza video il volto venga dapprima individuato semiautomaticamente, e come poi alcuni suoi elementi caratteristici siano localizzati nel tempo tramite un algoritmo automatico di messa in corrispondenza di modelli (template matching) permettendo di seguirne la posizione. Spieghiamo quindi le normalizzazioni geometriche dei segnali che abbiamo ricavato, il calcolo dei vettori caratteristici, ed il modo in cui questi sono utilizzati per stimare i modelli degli utilizzatori, approssimandoli tramite delle misture di distribuzioni gaussiane (Gaussian mixture models). Alla fine otteniamo l'identificazione e la verifica dell'identità della persona applicando la teoria delle probabilità e la regola di decisione o inferenza bayesiana. In seguito proponiamo un'estensione multimodale del nostro sistema di riconoscimento della persona; più precisamente, tramite un approccio probabilistico unificato, integriamo l'informazione sul movimento della testa con quelle relative al movimento della bocca e all'aspetto del viso. Infatti sviluppiamo un nuovo sottosistema temporale che possiede uno spazio caratteristico esteso, arricchito di alcuni parametri aggiuntivi legati al movimento della bocca; contemporaneamente, introduciamo un sottosistema spaziale complementare al precedente, basato su un'estensione probabilistica dell'approccio Eigenfaces originale. Alla fine implementiamo uno stadio di fusione, che metta insieme i valori di somiglianza dei due sottosistemi paralleli, attraverso un'appropriata strategia di fusione delle opinioni. Infine investighiamo un metodo pratico per estrarre nuovi parametri spazio-temporali relativi al volto a partire da sequenze video, i quali sono utilizzati per distinguere l'identità ed il sesso della persona. A questo riguardo sviluppiamo un sistema di riconoscimento chiamato tomovolti (tomofaces), il quale utilizza la tecnica della tomografia video per riassumere in una sola immagine l'informazione relativa all'aspetto ed al movimento del volto di una persona. Poi descriviamo la proiezione lineare dallo spazio dell'immagine ai raggi X ad un spazio caratteristico di dimensione ridotta, la stima dei modelli degli utilizzatori attraverso il calcolo dei rappresentanti corrispondenti ad ogni cluster, ed il riconoscimento dell'identità e del genere attraverso un classificatore al vicino più prossimo (nearest neighbour classifier), che adopera le distanze nel sottospazio
Samir, Chafik. "Analyse des déformations des visages 3D utilisant les chemins géodésiques dans l'espace des surfaces faciales". Evry, Institut national des télécommunications, 2006. http://www.theses.fr/2007TELE0016.
Testo completo[non communiqué]
Dantcheva, Antitza. "Biométries faciales douces : méthodes, applications et défis". Phd thesis, Paris, Télécom ParisTech, 2011. https://pastel.hal.science/pastel-00673146.
Testo completoThis dissertation studies soft biometrics traits, their applicability in different security and commercial scenarios, as well as related usability aspects. We place the emphasis on human facial soft biometric traits which constitute the set of physical, adhered or behavioral human characteristics that can partially differentiate, classify and identify humans. Such traits, which include characteristics like age, gender, skin and eye color, the presence of glasses, moustache or beard, inherit several advantages such as ease of acquisition, as well as a natural compatibility with how humans perceive their surroundings. Specifically, soft biometric traits are compatible with the human process of classifying and recalling our environment, a process which involves constructions of hierarchical structures of different refined traits. This thesis explores these traits, and their application in soft biometric systems (SBSs), and specifically focuses on how such systems can achieve different goals including database search pruning, human identification, human re–identification and, on a different note, prediction and quantification of facial aesthetics. Our motivation originates from the emerging importance of such applications in our evolving society, as well as from the practicality of such systems. SBSs generally benefit from the non-intrusive nature of acquiring soft biometric traits, and enjoy computational efficiency which in turn allows for fast, enrolment–free and pose–flexible biometric analysis, even in the absence of consent and cooperation by the involved human subjects
Dantcheva, Antitza. "Biométries faciales douces : méthodes, applications et défis". Phd thesis, Télécom ParisTech, 2011. http://pastel.archives-ouvertes.fr/pastel-00673146.
Testo completoMaalej, Ahmed. "3D Facial Expressions Recognition Using Shape Analysis and Machine Learning". Thesis, Lille 1, 2012. http://www.theses.fr/2012LIL10025/document.
Testo completoFacial expression recognition is a challenging task, which has received growing interest within the research community, impacting important applications in fields related to human machine interaction (HMI). Toward building human-like emotionally intelligent HMI devices, scientists are trying to include the essence of human emotional state in such systems. The recent development of 3D acquisition sensors has made 3D data more available, and this kind of data comes to alleviate the problems inherent in 2D data such as illumination, pose and scale variations as well as low resolution. Several 3D facial databases are publicly available for the researchers in the field of face and facial expression recognition to validate and evaluate their approaches. This thesis deals with facial expression recognition (FER) problem and proposes an approach based on shape analysis to handle both static and dynamic FER tasks. Our approach includes the following steps: first, a curve-based representation of the 3D face model is proposed to describe facial features. Then, once these curves are extracted, their shape information is quantified using a Riemannain framework. We end up with similarity scores between different facial local shapes constituting feature vectors associated with each facial surface. Afterwards, these features are used as entry parameters to some machine learning and classification algorithms to recognize expressions. Exhaustive experiments are derived to validate our approach and results are presented and compared to the related work achievements
Dahmani, Sara. "Synthèse audiovisuelle de la parole expressive : modélisation des émotions par apprentissage profond". Electronic Thesis or Diss., Université de Lorraine, 2020. http://www.theses.fr/2020LORR0137.
Testo completo: The work of this thesis concerns the modeling of emotions for expressive audiovisual textto-speech synthesis. Today, the results of text-to-speech synthesis systems are of good quality, however audiovisual synthesis remains an open issue and expressive synthesis is even less studied. As part of this thesis, we present an emotions modeling method which is malleable and flexible, and allows us to mix emotions as we mix shades on a palette of colors. In the first part, we present and study two expressive corpora that we have built. The recording strategy and the expressive content of these corpora are analyzed to validate their use for the purpose of audiovisual speech synthesis. In the second part, we present two neural architectures for speech synthesis. We used these two architectures to model three aspects of speech : 1) the duration of sounds, 2) the acoustic modality and 3) the visual modality. First, we use a fully connected architecture. This architecture allowed us to study the behavior of neural networks when dealing with different contextual and linguistic descriptors. We were also able to analyze, with objective measures, the network’s ability to model emotions. The second neural architecture proposed is a variational auto-encoder. This architecture is able to learn a latent representation of emotions without using emotion labels. After analyzing the latent space of emotions, we presented a procedure for structuring it in order to move from a discrete representation of emotions to a continuous one. We were able to validate, through perceptual experiments, the ability of our system to generate emotions, nuances of emotions and mixtures of emotions, and this for expressive audiovisual text-to-speech synthesis
Soladié, Catherine. "Représentation Invariante des Expressions Faciales". Phd thesis, Université Rennes 1, 2013. http://tel.archives-ouvertes.fr/tel-00935973.
Testo completoBaklouti, Malek. "Localisation du visage et extraction des éléments faciaux, pour la conception d'un mode d'interaction homme-machine". Versailles-St Quentin en Yvelines, 2009. http://www.theses.fr/2009VERS0035.
Testo completoThis work deals with Human-Machine Interface for assistive robotic systems. Assistive systems should be endowed with interfaces that are specifically designed for disabled people in order to enable them to control the system with the most natural and less tiring way. This is the primary concern of this work. More precisely, we were interested in developing a vision based interface using user’s head movement. The problem was tackled incrementally following the system used: monocular and stereoscopic camera. Using monocular camera, we proposed a new approach for learning faces using a committee of neural networks generated using the well known Adaboost. We proposed training the neural network with reduced space Haar-like features instead of working with image pixels themselves. In the second part, we are proposing to tackle the head pose estimation in its fine level using stereo vision approach. The framework can be break down into two parts: The first part consists in estimating the 3D points set using stereoscopic acquisition and the second one deals with aligning a Candide-1 model with the 3D points set. Under alignment, the transformation matrix of the Candide model corresponds to the head pose parameters
Dapogny, Arnaud. "A walk through randomness for face analysis in unconstrained environments". Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066662/document.
Testo completoAutomatic face analysis is a key to the development of intelligent human-computer interaction systems and behavior understanding. However, there exist a number of factors that makes face analysis a difficult problem. This include morphological differences between different persons, head pose variations as well as the possibility of partial occlusions. In this PhD, we propose a number of adaptations of the so-called Random Forest algorithm to specifically adress those problems. Mainly, those improvements consist in:– The development of a Pairwise Conditional Random Forest framework, that consists in training Random Forests upon pairs of expressive images. Pairwise trees are conditionned on the expression label of the first frame of a pair to reduce the ongoing expression transition variability. Additionnally, trees can be conditionned upon a head pose estimate to peform facial expression recognition from an arbitrary viewpoint.– The design of a hierarchical autoencoder network to model the local face texture patterns. The reconstruction error of this network provides a confidence measurement that can be used to weight Randomized decision trees trained on spatially-defined local subspace of the face. Thus, we can provide an expression prediction that is robust to partial occlusions.– Improvements over the very recent Neural Decision Forests framework, that include both a simplified training procedure as well as a new greedy evaluation procedure, that allows to dramatically improve the evaluation runtime, with applications for online learning and, deep learning convolutional neural network-based features for facial expression recognition as well as feature point alignement
Alioua, Nawal. "Extraction et analyse des caractéristiques faciales : application à l'hypovigilance chez le conducteur". Thesis, Rouen, INSA, 2015. http://www.theses.fr/2015ISAM0002/document.
Testo completoStudying facial features has attracted increasing attention in both academic and industrial communities. Indeed, these features convey nonverbal information that plays a key role in humancommunication. Moreover, they are very useful to allow human-machine interactions. Therefore, the automatic study of facial features is an important task for various applications includingrobotics, human-machine interfaces, behavioral science, clinical practice and monitoring driver state. In this thesis, we focus our attention on monitoring driver state through its facial features analysis. This problematic solicits a universal interest caused by the increasing number of road accidents, principally induced by deterioration in the driver vigilance level, known as hypovigilance. Indeed, we can distinguish three hypovigilance states. The first and most critical one is drowsiness, which is manifested by an inability to keep awake and it is characterized by microsleep intervals of 2-6 seconds. The second one is fatigue, which is defined by the increasing difficulty of maintaining a task and it is characterized by an important number of yawns. The third and last one is the inattention that occurs when the attention is diverted from the driving activity and it is characterized by maintaining the head pose in a non-frontal direction.The aim of this thesis is to propose facial features based approaches allowing to identify driver hypovigilance. The first approach was proposed to detect drowsiness by identifying microsleepintervals through eye state analysis. The second one was developed to identify fatigue by detecting yawning through mouth analysis. Since no public hypovigilance database is available,we have acquired and annotated our own database representing different subjects simulating hypovigilance under real lighting conditions to evaluate the performance of these two approaches. Next, we have developed two driver head pose estimation approaches to detect its inattention and also to determine its vigilance level even if the facial features (eyes and mouth) cannot be analyzed because of non-frontal head positions. We evaluated these two estimators on the public database Pointing'04. Then, we have acquired and annotated a driver head pose database to evaluate our estimators in real driving conditions
Ni, Weiyuan. "Recalage d'images de visage". Thesis, Grenoble, 2012. http://www.theses.fr/2012GRENT045/document.
Testo completoFace alignment is an important step in a typical automatic face recognition system.This thesis addresses the alignment of faces for face recognition applicationin video surveillance context. The main challenging factors of this research includethe low quality of images (e.g., low resolution, motion blur, and noise), uncontrolledillumination conditions, pose variations, expression changes, and occlusions. In orderto deal with these problems, we propose several face alignment methods using differentstrategies. The _rst part of our work is a three-stage method for facial pointlocalization which can be used for correcting mis-alignment errors. While existingalgorithms mostly rely on a priori knowledge of facial structure and on a trainingphase, our approach works in an online mode without requirements of pre-de_nedconstraints on feature distributions. The proposed method works well on images underexpression and lighting variations. The key contributions of this thesis are aboutjoint image alignment algorithms where a set of images is simultaneously alignedwithout a biased template selection. We respectively propose two unsupervised jointalignment algorithms : \Lucas-Kanade entropy congealing" (LKC) and \gradient correlationcongealing" (GCC). In LKC, an image ensemble is aligned by minimizing asum-of-entropy function de_ned over all images. GCC uses gradient correlation coef-_cient as similarity measure. The proposed algorithms perform well on images underdi_erent conditions. To further improve the robustness to mis-alignments and thecomputational speed, we apply a multi-resolution framework to joint face alignmentalgorithms. Moreover, our work is not limited in the face alignment stage. Since facealignment and face acquisition are interrelated, we develop an adaptive appearanceface tracking method with alignment feedbacks. This closed-loop framework showsits robustness to large variations in target's state, and it signi_cantly decreases themis-alignment errors in tracked faces
Dahmane, Mohamed. "Analyse de mouvements faciaux à partir d'images vidéo". Thèse, 2011. http://hdl.handle.net/1866/7120.
Testo completoIn a face-to-face talk, language is supported by nonverbal communication, which plays a central role in human social behavior by adding cues to the meaning of speech, providing feedback, and managing synchronization. Information about the emotional state of a person is usually carried out by facial attributes. In fact, 55% of a message is communicated by facial expressions whereas only 7% is due to linguistic language and 38% to paralanguage. However, there are currently no established instruments to measure such behavior. The computer vision community is therefore interested in the development of automated techniques for prototypic facial expression analysis, for human computer interaction applications, meeting video analysis, security and clinical applications. For gathering observable cues, we try to design, in this research, a framework that can build a relatively comprehensive source of visual information, which will be able to distinguish the facial deformations, thus allowing to point out the presence or absence of a particular facial action. A detailed review of identified techniques led us to explore two different approaches. The first approach involves appearance modeling, in which we use the gradient orientations to generate a dense representation of facial attributes. Besides the facial representation problem, the main difficulty of a system, which is intended to be general, is the implementation of a generic model independent of individual identity, face geometry and size. We therefore introduce a concept of prototypic referential mapping through a SIFT-flow registration that demonstrates, in this thesis, its superiority to the conventional eyes-based alignment. In a second approach, we use a geometric model through which the facial primitives are represented by Gabor filtering. Motivated by the fact that facial expressions are not only ambiguous and inconsistent across human but also dependent on the behavioral context; in this approach, we present a personalized facial expression recognition system whose overall performance is directly related to the localization performance of a set of facial fiducial points. These points are tracked through a sequence of video frames by a modification of a fast Gabor phase-based disparity estimation technique. In this thesis, we revisit the confidence measure, and introduce an iterative conditional procedure for displacement estimation that improves the robustness of the original methods.