Spectrum analysis methods for 3D facial expression recognition and head pose estimation
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Sukno, Federico Mateo
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150 p.
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Derkach, D. Spectrum analysis methods for 3D facial expression recognition and head pose estimation. Universitat Pompeu Fabra; 2018. handle: http://hdl.handle.net/10803/664578
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Doctoral program
Programa de doctorat en Tecnologies de la Informació i les Comunicacions
Abstract
Al llarg de les últimes dècades, l'anàlisi facial ha atret un interès creixent i considerable per part de la comunitat investigadora amb l’objectiu de millorar la interacció i la cooperació entre les persones i les màquines. Aquest interès ha propiciat la creació de sistemes automàtics capaços de reaccionar a diversos estímuls com ara els moviments del cap o les emocions d’una persona. Més enllà, les tasques automatitzades s’han de poder realitzar amb gran precisió dins d’entorns no controlats, fet que ressalta la necessitat d'algoritmes que aprofitin al màxim els avantatges que proporcionen les dades 3D. Aquests sistemes poden ser útils en molts àmbits com ara la interacció home-màquina, tutories, entrevistes, atenció sanitària, màrqueting, etc. En aquesta tesi, ens centrem en dos aspectes de l'anàlisi facial: el reconeixement d'expressions i l'estimació de l'orientació del cap. En ambdós casos, ens enfoquem en l’ús de dades 3D i presentem contribucions que tenen com a objectiu la identificació de representacions significatives de la geometria facial mitjançant mètodes basats en la descomposició espectral: 1. Proposem una tecnologia basada en la representació espectral per al reconeixement d’expressions facials utilitzant exclusivament la geometria 3D, la qual ens permet una descripció completa de la superfície subjacent que pot ser ajustada al nivell de detall desitjat. Dita tecnologia, es basa en la descomposició de fragments locals de la superfície en les seves components de freqüència espacial, d’una manera semblant a la transformada de Fourier, que estan relacionades amb característiques intrínseques de la superfície. Concretament, proposem la utilització de les Graph Laplacian Features (GLFs) que resulten de la projecció dels fragments locals de la superfície a una base comuna obtinguda a partir del Graph Laplacian eigenspace. El mètode proposat s’ha avaluat en termes de reconeixement d’expressions i Action Units (activacions musculars facials), i els resultats obtinguts confirmen que les GLFs produeixen taxes de reconeixement comparables a l’estat de l’art. 2. Proposem un mètode per a l’estimació de l’orientació del cap que permet modelar el manifold subjacent que formen les rotacions generals en 3D. En primer lloc, construïm un sistema completament automàtic que combina la detecció de landmarks (punts facials rellevants) i característiques basades en diccionari, el qual ha obtingut els millors resultats al FG2017 Head Pose Estimation Challenge. Posteriorment, utilitzem una representació basada en tensors i la seva descomposició en els valors singulars d’ordre més alt per tal de separar els subespais de cada factor de rotació i mostrar que cada un d’ells té una estructura clara que pot ser modelada amb funcions trigonomètriques. Aquesta representació proporciona un coneixement detallat del comportament de les dades i pot ser utilitzada per millorar l’estimació de les orientacions dels angles del cap.
Facial analysis has attracted considerable research efforts over the last decades, with a growing interest in improving the interaction and cooperation between people and computers. This makes it necessary that automatic systems are able to react to things such as the head movements of a user or his/her emotions. Further, this should be done accurately and in unconstrained environments, which highlights the need for algorithms that can take full advantage of 3D data. These systems could be useful in multiple domains such as human-computer interaction, tutoring, interviewing, health-care, marketing etc. In this thesis, we focus on two aspects of facial analysis: expression recognition and head pose estimation. In both cases, we specifically target the use of 3D data and present contributions that aim to identify meaningful representations of the facial geometry based on spectral decomposition methods: 1. We propose a spectral representation framework for facial expression recognition using exclusively 3D geometry, which allows a complete description of the underlying surface that can be further tuned to the desired level of detail. It is based on the decomposition of local surface patches in their spatial frequency components, much like a Fourier transform, which are related to intrinsic characteristics of the surface. We propose the use of Graph Laplacian Features (GLFs), which result from the projection of local surface patches into a common basis obtained from the Graph Laplacian eigenspace. The proposed approach is tested in terms of expression and Action Unit recognition and results confirm that the proposed GLFs produce state-of-the-art recognition rates. 2. We propose an approach for head pose estimation that allows modeling the underlying manifold that results from general rotations in 3D. We start by building a fully-automatic system based on the combination of landmark detection and dictionary-based features, which obtained the best results in the FG2017 Head Pose Estimation Challenge. Then, we use tensor representation and higher order singular value decomposition to separate the subspaces that correspond to each rotation factor and show that each of them has a clear structure that can be modeled with trigonometric functions. Such representation provides a deep understanding of data behavior, and can be used to further improve the estimation of the head pose angles.
Facial analysis has attracted considerable research efforts over the last decades, with a growing interest in improving the interaction and cooperation between people and computers. This makes it necessary that automatic systems are able to react to things such as the head movements of a user or his/her emotions. Further, this should be done accurately and in unconstrained environments, which highlights the need for algorithms that can take full advantage of 3D data. These systems could be useful in multiple domains such as human-computer interaction, tutoring, interviewing, health-care, marketing etc. In this thesis, we focus on two aspects of facial analysis: expression recognition and head pose estimation. In both cases, we specifically target the use of 3D data and present contributions that aim to identify meaningful representations of the facial geometry based on spectral decomposition methods: 1. We propose a spectral representation framework for facial expression recognition using exclusively 3D geometry, which allows a complete description of the underlying surface that can be further tuned to the desired level of detail. It is based on the decomposition of local surface patches in their spatial frequency components, much like a Fourier transform, which are related to intrinsic characteristics of the surface. We propose the use of Graph Laplacian Features (GLFs), which result from the projection of local surface patches into a common basis obtained from the Graph Laplacian eigenspace. The proposed approach is tested in terms of expression and Action Unit recognition and results confirm that the proposed GLFs produce state-of-the-art recognition rates. 2. We propose an approach for head pose estimation that allows modeling the underlying manifold that results from general rotations in 3D. We start by building a fully-automatic system based on the combination of landmark detection and dictionary-based features, which obtained the best results in the FG2017 Head Pose Estimation Challenge. Then, we use tensor representation and higher order singular value decomposition to separate the subspaces that correspond to each rotation factor and show that each of them has a clear structure that can be modeled with trigonometric functions. Such representation provides a deep understanding of data behavior, and can be used to further improve the estimation of the head pose angles.
Keywords
Facial expression recognition, 3D face, Spectral shape analysis, Laplace operators, 3D head pose, Manifold learning, Tensor decomposition, Non-linear manifold modeling
Subjects
62 - Engineering. Technology
Publisher
Universitat Pompeu Fabra
