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Modern approaches to second language (L2) pedagogy emphasise authentic L2 use for successful L2 learning. However, it has been a common observation L2 contexts that despite possessing years of experience in L2 learning, sometimes even students with higher L2 proficiency shy away from communication, while others plunge themselves in a conversation involving L2. It has been argued that in order for learners to become successful L2 users they not only have to have competence and motivation but a higher willingness to communicate in the target language as well. The present study was, therefore, designed to explore the situational variables and their interaction determining the willingness to communicate (WTC) in English (L2) of six postgraduate business students in a university classroom in Pakistan. While much research has been undertaken to examine trait-like psychological antecedents, only a few studies have examined the nature of the interaction between situational variables affecting L2 WTC in a classroom context. Adopting the Dynamic Systems Theory (DST) framework, the present study utilised classroom observations, learners’ diaries, stimulated recall interviews and biographic questionnaires to collect the data over ten weeks. The study results showed that participants’ L2 WTC emerged as a result of the complex, dynamic and non-linear interaction between contextual, psychological, linguistic and physiological factors. Most notably, the study revealed that while learners’ L2 use was a manifestation of their willingness to talk, their silence was not always a reflection of their un-willingness but involved an active cognitive engagement in L2 communication. The current study, therefore, reinforces the need for teachers to be aware of the multiple factors which affect learners’ L2 WTC and silence in L2 classroom. Owing to the complexity and non-linearity of interaction of variables, the current study proposes pre-service and in-service teacher training for English language teachers in Pakistan.

This thesis investigates the long standing problem of 3D reconstruction from a single 2D face image. Face reconstruction from a single 2D face image is an ill posed problem involving estimation of the intrinsic and the extrinsic camera parameters, light parameters, shape parameters and the texture parameters. The proposed approach has many potential applications in the law enforcement, surveillance, medicine, computer games and the entertainment industries. This problem is addressed using an analysis by synthesis framework by reconstructing a 3D face model from identity photographs. The identity photographs are a widely used medium for face identi cation and can be found on identity cards and passports. The novel contribution of this thesis is a new technique for creating 3D face models from a single 2D face image. The proposed method uses the improved dense 3D correspondence obtained using rigid and non-rigid registration techniques. The existing reconstruction methods use the optical ow method for establishing 3D correspondence. The resulting 3D face database is used to create a statistical shape model. The existing reconstruction algorithms recover shape by optimizing over all the parameters simultaneously. The proposed algorithm simplifies the reconstruction problem by using a step wise approach thus reducing the dimension of the parameter space and simplifying the opti- mization problem. In the alignment step, a generic 3D face is aligned with the given 2D face image by using anatomical landmarks. The texture is then warped onto the 3D model by using the spatial alignment obtained previously. The 3D shape is then recovered by optimizing over the shape parameters while matching a texture mapped model to the target image. There are a number of advantages of this approach. Firstly, it simpli es the optimization requirements and makes the optimization more robust. Second, there is no need to accurately recover the illumination parameters. Thirdly, there is no need for recovering the texture parameters by using a texture synthesis approach. Fourthly, quantitative analysis is used for improving the quality of reconstruction by improving the cost function. Previous methods use qualitative methods such as visual analysis, and face recognition rates for evaluating reconstruction accuracy. The improvement in the performance of the cost function occurs as a result of improvement in the feature space comprising the landmark and intensity features. Previously, the feature space has not been evaluated with respect to reconstruction accuracy thus leading to inaccurate assumptions about its behaviour. The proposed approach simpli es the reconstruction problem by using only identity images, rather than placing eff ort on overcoming the pose, illumination and expression (PIE) variations. This makes sense, as frontal face images under standard illumination conditions are widely available and could be utilized for accurate reconstruction. The reconstructed 3D models with texture can then be used for overcoming the PIE variations.