This Ph.D thesis work is dedicated to automatic facial analysis in 3D, including facial landmarking and facial expression recognition. Indeed, facial expression plays an important role both in verbal and non verbal communication, and in expressing emotions. Thus, automatic facial expression recognition has various purposes and applications and particularly is at the heart of "intelligent" human-centered human/computer(robot) interfaces. Meanwhile, automatic landmarking provides aprior knowledge on location of face landmarks, which is required by many face analysis methods such as face segmentation and feature extraction used for instance for expression recognition. The purpose of this thesis is thus to elaborate 3D landmarking and facial expression recognition approaches for finally proposing an automatic facial activity (facial expression and action unit) recognition solution.In this work, we have proposed a Bayesian Belief Network (BBN) for recognizing facial activities, such as facial expressions and facial action units. A StatisticalFacial feAture Model (SFAM) has also been designed to first automatically locateface landmarks so that a fully automatic facial expression recognition system can be formed by combining the SFAM and the BBN. The key contributions are the followings. First, we have proposed to build a morphable partial face model, named SFAM, based on Principle Component Analysis. This model allows to learn boththe global variations in face landmark configuration and the local ones in terms of texture and local geometry around each landmark. Various partial face instances can be generated from SFAM by varying model parameters. Secondly, we have developed a landmarking algorithm based on the minimization an objective function describing the correlation between model instances and query faces. Thirdly, we have designed a Bayesian Belief Network with a structure describing the casual relationships among subjects, expressions and facial features. Facial expression oraction units are modelled as the states of the expression node and are recognized by identifying the maximum of beliefs of all states. We have also proposed a novel method for BBN parameter inference using a statistical feature model that can beconsidered as an extension of SFAM. Finally, in order to enrich information usedfor 3D face analysis, and particularly 3D facial expression recognition, we have also elaborated a 3D face feature, named SGAND, to characterize the geometry property of a point on 3D face mesh using its surrounding points.The effectiveness of all these methods has been evaluated on FRGC, BU3DFEand Bosphorus datasets for facial landmarking as well as BU3DFE and Bosphorus datasets for facial activity (expression and action unit) recognition.

This thesis presents an evaluation of the effectiveness of an agroforestry extension program to smallholder farmers on Leyte Island, the Philippines. The imperative for reforestation is well recognised in the Philippines and was the impetus for this program which provided farmers with assistance to establish and silviculturally manage timber trees on their land. Because the cost-effectiveness of agroforestry extension is increased if farmers develop self-efficacy without extensive training, the extension program was offered in two regimes to test the necessity for extended assistance. In the extended assistance regime, farmers were offered on-site assistance to collect seed, grow seedlings, prepare sites and establish trees, whereas in the limited assistance regime, farmers were only offered assistance to collect seed and grow seedlings. Descriptive statistics were collected of farmers’ acceptance of technology and the manner in which technology was adapted to suit their personal circumstances. Translated conversations between farmers and extension staff also provided a rich source of data which provided insights into farmers’ motivation. Extension activities were reviewed at a mid-program workshop, a final on-site inspection and an end-of-program workshop. Farmers responded positively to the extended assistance program which helped them to grow and out-plant seedlings. The limited assistance program was relatively unsuccessful. Overall, the extension program was successful in shifting the initiative for further planting from extension staff to participating farmers. However, farmers showed little interest in applying silvicultural thinning or pruning to existing plantations of trees because extension advice was not congruent with their existing mental models of these procedures. Systems modelling of socio-economic variables which had been found to affect program outcomes was used to predict critical success factors. A key constraint to program recruitment was found to be farmers’ perception of harvest security, even when their needs for technology and planting materials are met. Modelling also cast doubt on the usefulness of written extension materials and emphasised the necessity for extended face-to-face technical assistance. Although conducted in Leyte, the findings of this research provide guidance for issues which affect the adoption of agroforestry both in the Philippines and in other countries. The research found that it was possible to recruit and motivate farmers without providing material incentives. If farmers experienced unexpected problems, providing extended face-to-face contact and assistance was critical if catastrophic losses of participating farmers were to be avoided. The failure of attempts to introduce advanced-age silviculture also indicated a need to elicit farmers’ mental models as a precursor or parallel enquiry to extension activities. In a situation where little was initially known about farmers’ understanding of agroforestry technology or the variables which affect their acceptance or rejection of extension assistance, the results of this research have shown that it is possible to build the capacity of farmers to establish timber trees. This result is in contrast to the acknowledged failure of the logging concession system in the Philippines and the difficulties faced by some industrial plantations and community-based programs. This investigation has shown that an opportunity exists to lift the level of tree planting in Leyte, provided that system variables which are either critical success factors or impediments are addressed.

This thesis presents an evaluation of the effectiveness of an agroforestry extension program to smallholder farmers on Leyte Island, the Philippines. The imperative for reforestation is well recognised in the Philippines and was the impetus for this program which provided farmers with assistance to establish and silviculturally manage timber trees on their land. Because the cost-effectiveness of agroforestry extension is increased if farmers develop self-efficacy without extensive training, the extension program was offered in two regimes to test the necessity for extended assistance. In the extended assistance regime, farmers were offered on-site assistance to collect seed, grow seedlings, prepare sites and establish trees, whereas in the limited assistance regime, farmers were only offered assistance to collect seed and grow seedlings. Descriptive statistics were collected of farmers’ acceptance of technology and the manner in which technology was adapted to suit their personal circumstances. Translated conversations between farmers and extension staff also provided a rich source of data which provided insights into farmers’ motivation. Extension activities were reviewed at a mid-program workshop, a final on-site inspection and an end-of-program workshop. Farmers responded positively to the extended assistance program which helped them to grow and out-plant seedlings. The limited assistance program was relatively unsuccessful. Overall, the extension program was successful in shifting the initiative for further planting from extension staff to participating farmers. However, farmers showed little interest in applying silvicultural thinning or pruning to existing plantations of trees because extension advice was not congruent with their existing mental models of these procedures. Systems modelling of socio-economic variables which had been found to affect program outcomes was used to predict critical success factors. A key constraint to program recruitment was found to be farmers’ perception of harvest security, even when their needs for technology and planting materials are met. Modelling also cast doubt on the usefulness of written extension materials and emphasised the necessity for extended face-to-face technical assistance. Although conducted in Leyte, the findings of this research provide guidance for issues which affect the adoption of agroforestry both in the Philippines and in other countries. The research found that it was possible to recruit and motivate farmers without providing material incentives. If farmers experienced unexpected problems, providing extended face-to-face contact and assistance was critical if catastrophic losses of participating farmers were to be avoided. The failure of attempts to introduce advanced-age silviculture also indicated a need to elicit farmers’ mental models as a precursor or parallel enquiry to extension activities. In a situation where little was initially known about farmers’ understanding of agroforestry technology or the variables which affect their acceptance or rejection of extension assistance, the results of this research have shown that it is possible to build the capacity of farmers to establish timber trees. This result is in contrast to the acknowledged failure of the logging concession system in the Philippines and the difficulties faced by some industrial plantations and community-based programs. This investigation has shown that an opportunity exists to lift the level of tree planting in Leyte, provided that system variables which are either critical success factors or impediments are addressed.

Cette thèse de doctorat est dédiée à l’analyse automatique de visages 3D, incluant la détection de points d’intérêt et la reconnaissance de l’expression faciale. En effet, l’expression faciale joue un rôle important dans la communication verbale et non verbale, ainsi que pour exprimer des émotions. Ainsi, la reconnaissance automatique de l’expression faciale offre de nombreuses opportunités et applications, et est en particulier au coeur d’interfaces homme-machine "intelligentes" centrées sur l’être humain. Par ailleurs, la détection automatique de points d’intérêt du visage (coins de la bouche et des yeux, ...) permet la localisation d’éléments du visage qui est essentielle pour de nombreuses méthodes d’analyse faciale telle que la segmentation du visage et l’extraction de descripteurs utilisée par exemple pour la reconnaissance de l’expression. L’objectif de cette thèse est donc d’élaborer des approches de détection de points d’intérêt sur les visages 3D et de reconnaissance de l’expression faciale pour finalement proposer une solution entièrement automatique de reconnaissance de l’activité faciale incluant l’expression et les unités d’action (ou Action Units). Dans ce travail, nous avons proposé un réseau de croyance bayésien (Bayesian Belief Network ou BBN) pour la reconnaissance d’expressions faciales ainsi que d’unités d’action. Un modèle statistique de caractéristiques faciales (Statistical Facial feAture Model ou SFAM) a également été élaboré pour permettre la localisation des points d’intérêt sur laquelle s’appuie notre BBN afin de permettre la mise en place d’un système entièrement automatique de reconnaissance de l’expression faciale. Nos principales contributions sont les suivantes. Tout d’abord, nous avons proposé un modèle de visage partiel déformable, nommé SFAM, basé sur le principe de l’analyse en composantes principales. Ce modèle permet d’apprendre à la fois les variations globales de la position relative des points d’intérêt du visage (configuration du visage) et les variations locales en terme de texture et de forme autour de chaque point d’intérêt. Différentes instances de visages partiels peuvent ainsi être produites en faisant varier les valeurs des paramètres du modèle. Deuxièmement, nous avons développé un algorithme de localisation des points d’intérêt du visage basé sur la minimisation d’une fonction objectif décrivant la corrélation entre les instances du modèle SFAM et les visages requête. Troisièmement, nous avons élaboré un réseau de croyance bayésien (BBN) dont la structure décrit les relations de dépendance entre les sujets, les expressions et les descripteurs faciaux. Les expressions faciales et les unités d’action sont alors modélisées comme les états du noeud correspondant à la variable expression et sont reconnues en identifiant le maximum de croyance pour tous les états. Nous avons également proposé une nouvelle approche pour l’inférence des paramètres du BBN utilisant un modèle de caractéristiques faciales pouvant être considéré comme une extension de SFAM. Finalement, afin d’enrichir l’information utilisée pour l’analyse de visages 3D, et particulièrement pour la reconnaissance de l’expression faciale, nous avons également élaboré un descripteur de visages 3D, nommé SGAND, pour caractériser les propriétés géométriques d’un point par rapport à son voisinage dans le nuage de points représentant un visage 3D. L’efficacité de ces méthodes a été évaluée sur les bases FRGC, BU3DFE et Bosphorus pour la localisation des points d’intérêt ainsi que sur les bases BU3DFE et Bosphorus pour la reconnaissance des expressions faciales et des unités d’action. / This Ph.D thesis work is dedicated to automatic facial analysis in 3D, including facial landmarking and facial expression recognition. Indeed, facial expression plays an important role both in verbal and non verbal communication, and in expressing emotions. Thus, automatic facial expression recognition has various purposes and applications and particularly is at the heart of "intelligent" human-centered human/computer(robot) interfaces. Meanwhile, automatic landmarking provides aprior knowledge on location of face landmarks, which is required by many face analysis methods such as face segmentation and feature extraction used for instance for expression recognition. The purpose of this thesis is thus to elaborate 3D landmarking and facial expression recognition approaches for finally proposing an automatic facial activity (facial expression and action unit) recognition solution.In this work, we have proposed a Bayesian Belief Network (BBN) for recognizing facial activities, such as facial expressions and facial action units. A StatisticalFacial feAture Model (SFAM) has also been designed to first automatically locateface landmarks so that a fully automatic facial expression recognition system can be formed by combining the SFAM and the BBN. The key contributions are the followings. First, we have proposed to build a morphable partial face model, named SFAM, based on Principle Component Analysis. This model allows to learn boththe global variations in face landmark configuration and the local ones in terms of texture and local geometry around each landmark. Various partial face instances can be generated from SFAM by varying model parameters. Secondly, we have developed a landmarking algorithm based on the minimization an objective function describing the correlation between model instances and query faces. Thirdly, we have designed a Bayesian Belief Network with a structure describing the casual relationships among subjects, expressions and facial features. Facial expression oraction units are modelled as the states of the expression node and are recognized by identifying the maximum of beliefs of all states. We have also proposed a novel method for BBN parameter inference using a statistical feature model that can beconsidered as an extension of SFAM. Finally, in order to enrich information usedfor 3D face analysis, and particularly 3D facial expression recognition, we have also elaborated a 3D face feature, named SGAND, to characterize the geometry property of a point on 3D face mesh using its surrounding points.The effectiveness of all these methods has been evaluated on FRGC, BU3DFEand Bosphorus datasets for facial landmarking as well as BU3DFE and Bosphorus datasets for facial activity (expression and action unit) recognition.