It is increasingly easy for interested parties to play a role in the development of predictive algorithms, with a range of available tools and platforms for building datasets, as well as for training and evaluating machine learning (ML) models. For this reason, it is essential to create awareness among practitioners on the ethical challenges, such as the presence of social bias in training data. We present RECANT (Raising Awareness of Social Bias in Crowdsourced Training Data), a tool that allows users to explore the behaviors of four biometric models -- predicting the gender and race, as well as the perceived attractiveness and trustworthiness, of the person depicted in an input image. These models have been trained on a crowdsourced dataset of passport-style people images, where crowd annotators described attributes of the images, and reported their own demographic characteristics. With RECANT, users can explore the correct and wrong predictions made by each model, when using different subsets of the data in training, based on annotator attributes. We present its features, along with sample exercises, as a hands-on tool for raising awareness of potential pitfalls in data practices surrounding ML.

Here, face images of a specific age class are translated to images of different age classes in an unsupervised manner that enables training on independent sets of images for each age class. In order to learn pairwise translations between age classes, we adopt the UNsupervised Image-to-image Translation framework that employs Variational AutoEncoders and Generative Adversarial Networks. By mapping face images of different age classes to shared latent representations, the most personalized and abstract facial characteristics are preserved. To effectively diffuse age class information, a pyramid of local, neighbour, and global encoders is employed so that the latent representations progressively cover an increased age range. The proposed framework is applied to the FGNET aging database and compared to state-of-the-art techniques and the ground truth. Appealing experimental results demonstrate the ability of the proposed method to efficiently capture both intense and subtle aging effects.

The current study concerns the identification of possible differences in perception between the virtual and the real world in terms of the effect on brain activity. For this reason, an EEG device was used to capture participants' brain activity in different brain areas during their exposure to different virtual and real environments. The environments considered in this study portray a classroom environment with a scenario suitable for teacher training and professional development. The first aim of the experiment is to investigate if exposure to a virtual environment can affect motor, cognitive or other function of the users, and the second aim is to test if the graphics content and nature of such an environment can influence the user experience. During the study, the optimum duration of exposure in a virtual environment was also assessed by measuring the time that the brain needs to perceive and adapt to the new state. Our results, consisting of EEG data analyzed in 10 Regions of Interest (ROIs) and responses from an Igroup Presence questionnaire, indicated a significant difference in each brain area, especially in the frontal and occipital region, when a participant was exposed to a non-realistic virtual environment, compared to a realistic one, highlighting the impact of the selected virtual environment design. The results of the experiment can play an important role in defining the characteristics of optimal virtual environments for virtual reality-based training applications.

Virtual Reality(VR) is still an emerging technology in terms of recognizing its full potential in education and specifically in teacher education. A key issue of a VR-based approach for teacher training, is the level of presence along with the empathy inflicted on the trainees that will allow them to experience realistic and emotion-rich classroom situations in a virtual environment. This paper describes an experiment that aims to assess the influence of the graphical realism of a virtual classroom to the levels of presence and development of empathy skills for trainee teachers. Moreover, a second objective is to investigate whether there are significant differences between training in a VR classroom and a real physical classroom and how this affects the trainee teacher. The overall conclusion of the experiment is that the design of the VR classroom environment influenced the levels of immersion and presence. Moreover, according to the results there are serious indications that the VR system provided users the immersion necessary for the development of embodied thinking skills and thus of empathy in relation to multiculturalism.

An important aspect related to the development of face-aging algorithms is the evaluation of the ability of such algorithms to produce accurate age-progressed faces. In most studies reported in the literature, the performance of face-aging systems is established based either on the judgment of human observers or by using machine-based evaluation methods. In this paper we perform an experimental evaluation that aims to assess the applicability of human-based against typical machine based performance evaluation methods. The results of our experiments indicate that machines can be more accurate in determining the performance of face-aging algorithms. Our work aims towards the development of a complete evaluation framework for age progression methodologies.

We describe a quantitative evaluation of the performance of different classifiers in the task of automatic age estimation. In this context, we generate a statistical model of facial appearance, which is subsequently used as the basis for obtaining a compact parametric description of face images. The aim of our work is to design classifiers that accept the model-based representation of unseen images and produce an estimate of the age of the person in the corresponding face image. For this application, we have tested different classifiers: a classifier based on the use of quadratic functions for modeling the relationship between face model parameters and age, a shortest distance classifier, and artificial neural network based classifiers. We also describe variations to the basic method where we use age-specific and/or appearance specific age estimation methods. In this context, we use age estimation classifiers for each age group and/or classifiers for different clusters of subjects within our training set. In those cases, part of the classification procedure is devoted to choosing the most appropriate classifier for the subject/age range in question, so that more accurate age estimates can be obtained. We also present comparative results concerning the performance of humans and computers in the task of age estimation. Our results indicate that machines can estimate the age of a person almost as reliably as humans.

The process of aging causes significant alterations in the facial appearance of individuals. When compared with other sources of variation in face images, appearance variation due to aging displays some unique characteristics. Changes in facial appearance due to aging can even affect discriminatory facial features, resulting in deterioration of the ability of humans and machines to identify aged individuals. We describe how the effects of aging on facial appearance can be explained using learned age transformations and present experimental results to show that reasonably accurate estimates of age can be made for unseen images. We also show that we can improve our results by taking into account the fact that different individuals age in different ways and by considering the effect of lifestyle. Our proposed framework can be used for simulating aging effects on new face images in order to predict how an individual might look like in the future or how he/she used to look in the past. The methodology presented has also been used for designing a face recognition system, robust to aging variation. In this context, the perceived age of the subjects in the training and test images is normalized before the training and classification procedure so that aging variation is eliminated. Experimental results demonstrate that, when age normalization is used, the performance of our face recognition system can be improved.

A large number of high-performance automatic face recognition systems have been reported in the literature. Many of them are robust to within class appearance variation of subjects such as variation in expression, lighting of subjects such as variation in expression, lighting and pose. However, most of the face identification systems developed are sensitive to changes in the age of individuals. We present experimental results to prove that the performance of automatic face recognition systems depends on the age difference of subjects between the training and test images. We also demonstrate that automatic age simulation techniques can be used for designing face recognition systems, robust to ageing variation. In this context, the perceived age of the subjects in the training and test images is modified before the training and classification procedures, so that ageing variation is eliminated. Experimental results demonstrate that the performance of our face recognition system can be improved significantly, when this approach is adopted.

The process of ageing causes significant alterations in the facial appearance of individuals. When compared with other sources of variation in face images (e.g. variation due to changes in pose and expression), appearance variation due to ageing displays some unique characteristics. For example ageing variation is specific to a given individual, it occurs slowly and it is affected significantly by other factors, such as the health, gender and the lifestyle of the individual. In this paper we describe how the effects of ageing on facial appearance can be explained using a parameterized statistical model. We present experimental results to show that reasonably accurate estimates of age can be made for unseen images. We also show that we can improve our results significantly by taking into account the fact that different individuals age in different ways and by considering the effect of lifestyle. We also demonstrate how the proposed framework can be used for simulating ageing effects on new face images, in order to predict how an individual might look like in the future, or how he/she used to look in the past. Experimental and visual results on simulation of age effects are presented.

Face images are difficult to interpret because they are highly variable. Sources of variability include individual appearance, 3D pose, facial expression, and lighting. We describe a compact parametrized model of facial appearance which takes into account all these sources of variability. The model represents both shape and gray-level appearance, and is created by performing a statistical analysis over a training set of face images. A robust multiresolution search algorithm is used to fit the model to faces in new images. This allows the main facial features to be located, and a set of shape, and gray-level appearance parameters to be recovered. A good approximation to a given face can be reconstructed using less than 100 of these parameters. This representation can be used for tasks such as image coding, person identification, 3D pose recovery, gender recognition, and expression recognition. Experimental results are presented for a database of 690 face images obtained under widely varying conditions of 3D pose, lighting, and facial expression. The system performs well on all the tasks listed above.

Model based approaches to the interpretation of face images have proved very successful. We have previously described statistically based models of face shape and grey-level appearance and shown how they can be used to perform various coding and interpretation tasks (Lanitis et al., 1995). In the paper we describe improved methods of modelling, which couple shape and grey-level information more directly than our existing methods, isolate the changes in appearance due to different sources of variability (person, expression, pose, lighting) and deal with nonlinear shape variation. We show that the new methods are better suited to interpretation and tracking tasks.

Objects of the same class often exhibit variation in shape. This shape variation has previously been modelled by means of point distribution models (PDMs) in which there is a linear relationship between a set of shape parameters and the positions of points on the shape. Here we present a new form of PDM, which uses a multilayer perceptron (MLP) to carry out nonlinear principal component analysis. We demonstrate that MLP-PDMs can model the shape variability in classes of object for which the linear model fails. We describe the use of MLP-PDMs in image search and present quantitative results for a practical application (face recognition), demonstrating the ability to locate image structures accurately starting from a very poor initial approximation to their pose and shape.

Hand gesture recognition from video images is of considerable interest as a means of providing simple and intuitive man-machine interfaces. Possible applications range from replacing the mouse as a pointing device to virtual reality and communication with the deaf. We describe an approach to tracking a hand in an image sequence and recognising, in each video frame, which of five gestures it has adopted. A statistically based Point Distribution Model (PDM) is used to provide a compact parametrised description of the shape of the hand for any of the gestures or the transitions between them. The values of the resulting shape parameters are used in a statistical classifier to identify gestures. The model can be used as a deformable template to track a hand through a video sequence but this proves unreliable. We describe how a set of models, one for each of the five gestures, can be used for tracking with the appropriate model selected automatically. We show that this results in reliable tracking and gesture recognition for two 'unseen' video sequences in which all the gestures are used.

We describe how to build statistically-based flexible models of the 3D structure of variable objects, given a training set of uncalibrated images. We assume that for each example object there are two labelled images taken from different viewpoints. From each image pair a 3D structure can be reconstructed, up to either an affine or projective transformation, depending on which camera model is used. The reconstructions are aligned by choosing the transformations which minimise the distances between matched points across the training set. A statistical analysis results in an estimate of the mean structure of the training examples and a compact parameterised model of the variability in shape across the training set. Experiments have been performed using pinhole and affine camera models. Results are presented for both synthetic data and real images.

Face images are difficult to interpret because they are highly variable. Sources of variability include individual appearance, 3D pose, facial expression and lighting. We describe a compact parametrised model of facial appearance which takes into account all these sources of variability. The model represents both shape and grey-level appearance and is created by performing a statistical analysis over a training set of face images. A robust multi-resolution search algorithm is used to fit the model to faces in new images. This allows the main facial features to be located and a set of shape and grey-level appearance parameters to be recovered. A good approximation to a given face can be reconstructed using less than 100 of these parameters. This representation can be used for tasks such as image coding, person identification, pose recovery, gender recognition and expression recognition. The system performs well on all the tasks listed above.>

We describe a multi-resolution technique for locating for variable structures in images. This is an extension of work on Active Shape Models (ASMs)- statistical models which iteratively deform to match image data. An ASM consists of a shape model controlling a set of landmark points, together with a statistical model of the grey-levels expected around each landmark. Both the shape model and the grey-level models are trained on sets of labelled example images. In order to apply a coarse-to-fine search strategy it is necessary to train a set of grey-level models for each landmark, one for every level of a multi-resolution image pyramid. During image search the model is started on the coarsest resolution image. As the search progresses it moves to finer and finer resolutions until no further improvement can be made. We describe an automatic technique for deciding when to:ss has converged. We demonstrate the ntitative experiments which show a sigi speed and quality of fit compared to previous methods.

We describe the use of flexible models for representing the shape and grey-level appearance of human faces. These models are controlled by a small number of parameters which can be used to code the overall appearance of a face for image compression and classification purposes. The model parameters control both inter-class and within-class variation. Discriminant analysis techniques are employed to enhance the effect of those parameters affecting inter-class variation, which are useful for classification. We have performed experiments on face coding and reconstruction and automatic face identification. Good recognition rates are obtained even when significant variation in lighting, expression and 3D viewpoint, is allowed. Human faces display significant variation in appearance due to changes in expression, 3D orientation, lighting conditions, hairstyles and so on. A successful automatic face identification system should be capable of suppressing the effect of these factors allowing any face image to be rendered expression-free with standardised 3D orientation and lighting. We describe how the variations in shape and grey-level appearance in face images can be modelled, and present results for a fully automatic face identification system which tolerates changes in expression, viewpoint and lighting.

We describe a multiresolution approach to image search using flexible shape models. This is an extension of work on active shape models (ASMs)-statistical models which iteratively deform to match image data. An ASM consists of a shape model controlling a set of landmark points, together with a statistical model of the grey-levels expected around each landmark. Both the shape model and the grey-level models are trained on sets of labelled example images. In order to apply a coarse-to-fine search strategy it is necessary to train a set of grey-level models for each landmark, one for every level of a multiresolution image pyramid. We demonstrate the approach and give results of quantitative experiments which show a significant increase in both speed, robustness and quality of fit compared to previous methods.

A technique for classifying variable objects using flexible template models is described.: is recognised as the input, plant seeds, handprinted characters and human faces; quantitative results are presented. 1

The authors describe a technique for building compact models of the shape and appearance of flexible objects seen in 2-D images. The models are derived from the statistics of sets of labeled images of example objects. Each model consists of a flexible shape template, describing how important points of the object can vary, and a statistical model of the expected grey levels in regions around each model point. Such models have proved useful in a wide variety of applications. A description is given on how the models can be used in local image search, and examples of their application are included.>