Julia Dancer

Abstract

World-Wide-Web, with website and webpage as a main interface, facilitates dissemination of important information. Hence it is crucial to optimize webpage design for better user interaction, which is primarily done by analyzing users’ behavior, especially users’ eye-gaze locations on the webpage. However, gathering these data is still considered to be labor and time intensive. In this work, we enable the development of automatic eye-gaze estimations given webpage screenshots as input by curating of a unified dataset that consists of webpage screenshots, eye-gaze heatmap and website’s layout information in the form of image and text masks. Our curated dataset allows us to propose a deep learning-based model that leverages on both webpage screenshot and content information (image and text spatial location), which are then combined through attention mechanism for effective eye-gaze prediction. In our experiment, we show benefits of careful fine-tuning using our unified dataset to improve accuracy of eye-gaze predictions. We further observe the capability of our model to focus on targeted areas (images and text) to achieve accurate eye-gaze area predictions. Finally, comparison with other alternatives shows state-of-the-art result of our approach, establishing a benchmark for webpage based eye-gaze prediction task.

Abstract

The interest in automatic emotion recognition and the larger field of Affective Computing has recently gained momentum. The current emergence of large, video-based affect datasets offering rich multi-modal inputs facilitates the development of deep learning-based models for automatic affect analysis However, recent approaches to process these modalities cannot fully exploit them due to the use of oversimplified fusion schemes. Furthermore, the efficient use of temporal information inherent to these huge data are largely unexplored hindering their potential progress. In this work, we propose a multi-modal, sequence-based neural network with gating mechanisms for affect recognition. Our model consists of three major networks: Firstly, a latent-feature generator that extracts compact representations from both modalities. Secondly, a multi-task discriminator that estimates both input identity and quadrant estimation. Thirdly, a sequence-based predictor with attention and gating mechanisms that effectively merges both modalities and uses this information through sequence modelling. Using SEMAINE and SEWA affect datasets, we observe the impact of both proposed methods with progressive increase in accuracy. We further show in our ablation studies how the internal attention weight and gating coefficient impact our models' estimates quality. Finally, we demonstrate state of the art accuracy through comparisons with current alternatives on both datasets.

Abstract

The human face is one of the most visible features of our unique identity as individuals. Interestingly, monozygotic twins share almost identical facial traits and the same DNA sequence but could exhibit differences in other biometrical parameters. The expansion of the world wide web and the possibility to exchange pictures of humans across the planet has increased the number of people identified online as virtual twins or doubles that are not family related. Herein, we have characterized in detail a set of “look-alike” humans, defined by facial recognition algorithms, for their multiomics landscape. We report that these individuals share similar genotypes and differ in their DNA methylation and microbiome landscape. These results not only provide insights about the genetics that determine our face but also might have implications for the establishment of other human anthropometric properties and even personality characteristics.

Abstract

Lie detection is considered a concern for everyone in their day-to-day life, given its impact on human interactions. Thus, people normally pay attention to both what their interlocutors are saying and to their visual appearance, including the face, to find any signs that indicate whether or not the person is telling the truth. While automatic lie detection may help us to understand these lying characteristics, current systems are still fairly limited, partly due to lack of adequate datasets to evaluate their performance in realistic scenarios. In this work, we collect an annotated dataset of facial images, comprising both 2D and 3D information of several participants during a card game that encourages players to lie. Using our collected dataset, we evaluate several types of machine learning-based lie detectors in terms of their generalization, in person-specific and cross-application experiments. We first extract both handcrafted and deep learning-based features as relevant visual inputs, then pass them into multiple types of classifier to predict respective lie/non-lie labels. Subsequently, we use several metrics to judge the models’ accuracy based on the models predictions and ground truth. In our experiment, we show that models based on deep learning achieve the highest accuracy, reaching up to 57% for the generalization task and 63% when applied to detect the lie to a single participant. We further highlight the limitation of the deep learning-based lie detector when dealing with cross-application lie detection tasks. Finally, this analysis along the proposed datasets would potentially be useful not only from the perspective of computational systems perspective (e.g., improving current automatic lie prediction accuracy), but also for other relevant application fields, such as health practitioners in general medical counselings, education in academic settings or finance in the banking sector, where close inspections and understandings of the actual intentions of individuals can be very important.

Abstract

Affective Computing has recently attracted the attention of the research community, due to its numerous applications in diverse areas. In this context, the emergence of video-based data allows to enrich the widely used spatial features with the inclusion of temporal information. However, such spatio-temporal modelling often results in very high-dimensional feature spaces and large volumes of data, making training difficult and time consuming. This paper addresses these shortcomings by proposing a novel model that efficiently extracts both spatial and temporal features of the data by means of its enhanced temporal modelling based on latent features. Our proposed model consists of three major networks, coined Generator, Discriminator, and Combiner, which are trained in an adversarial setting combined with curriculum learning to enable our adaptive attention modules. In our experiments, we show the effectiveness of our approach by reporting our competitive results on both the AFEW-VA and SEWA datasets, suggesting that temporal modelling improves the affect estimates both in qualitative and quantitative terms. Furthermore, we find that the inclusion of attention mechanisms leads to the highest accuracy improvements, as its weights seem to correlate well with the appearance of facial movements, both in terms of temporal localisation and intensity. Finally, we observe the sequence length of around 160 ms to be the optimum one for temporal modelling, which is consistent with other relevant findings utilising similar lengths.

Abstract

Facial alignment is an essential task for many higher level facial analysis applications, such as animation, human activity recognition and human - computer interaction. Although the recent availability of big datasets and powerful deep-learning approaches have enabled major improvements on the state of the art accuracy, the performance of current approaches can severely deteriorate when dealing with images in highly unconstrained conditions, which limits the real-life applicability of such models. In this paper, we propose a composite recurrent tracker with internal denoising that jointly address both single image facial alignment and deformable facial tracking in the wild. Specifically, we incorporate multilayer LSTMs to model temporal dependencies with variable length and introduce an internal denoiser which selectively enhances the input images to improve the robustness of our overall model. We achieve this by combining 4 different sub-networks that specialize in each of the key tasks that are required, namely face detection, bounding-box tracking, facial region validation and facial alignment with internal denoising. These blocks are endowed with novel algorithms resulting in a facial tracker that is both accurate, robust to in-the-wild settings and resilient against drifting. We demonstrate this by testing our model on 300-W and Menpo datasets for single image facial alignment, and 300-VW dataset for deformable facial tracking. Comparison against 20 other state of the art methods demonstrates the excellent performance of the proposed approach.

Abstract

There is a growing interest in affective computing research nowadays given its crucial role in bridging humans with computers. This progress has recently been accelerated due to the emergence of bigger dataset. One recent advance in this field is the use of adversarial learning to improve model learning through augmented samples. However, the use of latent features, which is feasible through adversarial learning, is not largely explored, yet. This technique may also improve the performance of affective models, as analogously demonstrated in related fields, such as computer vision. To expand this analysis, in this work, we explore the use of latent features through our proposed adversarial-based networks for valence and arousal recognition in the wild. Specifically, our models operate by aggregating several modalities to our discriminator, which is further conditioned to the extracted latent features by the generator. Our experiments on the recently released SEWA dataset suggest the progressive improvements of our results. Finally, we show our competitive results on the Affective Behavior Analysis in-the-Wild (ABAW) challenge dataset.

Abstract

Increasing interest in complementary therapies prompts analysis of the objective impact on human physiology. Polarity Therapy (PT) is a branch of complementary medicine that relates to energy field therapies. Although previous clinical work has provided evidence of the impact on patients, the present work analyzes, for the first time, a short-term systematic investigation of such therapy. Several physiological signals were collected from 25 consecutive chronic anxiety patients seen in an outpatient clinic before and after PT, which included electrocardiographic analysis (ECG), galvanic skin response (GSR), blood volume pulse (BVP), and temperature. Also included was the analysis of facial expressions using state of the art deep learning-based models for automatic valence and arousal estimations. Using the recorded samples, we proceeded to calculate heart rate variability (HRV) analysis in a temporal, frequency and non-linear domain, which proves assessment of the autonomous nervous system. Fine analysis of the ECG was developed using wavelet-based techniques. The area under the T wave, recently described to correlate with electromagnetic ventricular activity, was also calculated. A psychological questionnaire assessed the clinical therapeutic impact before and after the session. Results revealed a positive impact of 30-minute therapy on blood pressure and heart rate reduction, direct evidence of down-regulation of sympathetic activity and up-regulation of parasympathetic activity, an inverse correlation of T-wave and BVP, which suggests improved cardiac coherence, and changes in facial expression which correlated with subjective perceptions of wellbeing. The combination of physiological variables, facial expression and self-assessment of wellbeing before and after a PT session revealed parallelism between the observed changes in physiological data and subjective feelings.

Abstract

In recent years, affective computing and its applications have become a fast-growing research topic. Furthermore, the rise of deep learning has introduced significant improvements in the emotion recognition system compared to classical methods. In this work, we propose a multi-modal emotion recognition model based on deep learning techniques using the combination of peripheral physiological signals and facial expressions. Moreover, we present an improvement to proposed models by introducing latent features extracted from our internal Bio Auto-Encoder (BAE). Both models are trained and evaluated on AMIGOS datasets reporting valence, arousal, and emotion state classification. Finally, to demonstrate a possible medical application in affective computing using deep learning techniques, we applied the proposed method to the assessment of anxiety therapy. To this purpose, a reduced multimodal database has been collected by recording facial expressions and peripheral signals such as electrocardiogram (ECG) and galvanic skin response (GSR) of each patient. Valence and arousal estimates were extracted using our proposed model across the duration of the therapy, with successful evaluation to the different emotional changes in the temporal domain.

Abstract

Human facial tracking is an important task in computer vision, which has recently lost pace compared to other facial analysis tasks. The majority of current available tracker possess two major limitations: their little use of temporal information and the widespread use of handcrafted features, without taking full advantage of the large annotated datasets that have recently become available. In this paper we present a fully end-to-end facial tracking model based on current state of the art deep model architectures that can be effectively trained from the available annotated facial landmark datasets. We build our model from the recently introduced general object tracker Re3, which allows modeling the short and long temporal dependency between frames by means of its internal Long Short Term Memory (LSTM) layers. Facial tracking experiments on the challenging 300-VW dataset show that our model can produce state of the art accuracy and far lower failure rates than competing approaches. We specifically compare the performance of our approach modified to work in tracking-by-detection mode and showed that, as such, it can produce results that are comparable to state of the art trackers. However, upon activation of our tracking mechanism, the results improve significantly, confirming the advantage of taking into account temporal dependencies.

Abstract

Automatic kinship recognition using Computer Vision, which aims to infer the blood relationship between individuals by only comparing their facial features, has started to gain attention recently. The introduction of large kinship datasets, such as Family In The Wild (FIW), has allowed large scale dataset modeling using state of the art deep learning models. Among other kinship recognition tasks, family classification task is lacking any significant progress due to its increasing difficulty in relation to the family member size. Furthermore, most current state of-the-art approaches do not perform any data pre-processing (which try to improve models accuracy) and are trained without a regularizer (which results in models susceptible to overfitting). In this paper, we present the Deep Family Classifier (DFC), a deep learning model for family classification in the wild. We build our model by combining two sub-networks: internal Image Feature Enhancer which operates by removing the image noise and provides an additional facial heatmap layer and Family Class Estimator trained with strong regularizers and a compound loss. We observe progressive improvement in accuracy during the validation phase, with a state of the art results of 16.89% is obtained for the track 2 in the RFIW2019 challenge and 17.08% of familly classification task on FIW dataset.

Abstract

The development of facial alignment models is growing rapidly thanks to the availability of large facial landmarked datasets and powerful deep learning models. However, important challenges still remain for facial alignment models to work on images under extreme conditions, such as severe occlusions or large variations in pose and illumination. Current attempts to overcome this limitation have mainly focused on building robust feature extractors with the assumption that the model will be able to discard the noise and select only the meaningful features. However, such an assumption ignores the importance of understanding the noise that characterizes unconstrained images, which has been shown to benefit computer vision models if used appropriately on the learning strategy. Thus, in this paper we investigate the introduction of specialized modules for noise detection and removal, in combination with our state-of-the-art facial alignment module and show that this leads to improved robustness both to synthesized noise and in-the-wild conditions. The proposed model is built by combining two major subnetworks: internal image denoiser (based on the Auto-Encoder architecture) and facial landmark localiser (based on the inception-resnet architecture). Our results on the 300-W and Menpo datasets show that our model can effectively handle different types of synthetic noise, which also leads to enhanced robustness in real-world unconstrained settings, reaching top state-of-the-art accuracy.

Abstract

With the increasing use of high resolution images for remote sensing image analysis, texture has become a critical characteristic to be utilized in improving classification. This paper investigates the application of a new robust and simple texture descriptor, Weber Local Descriptor (WLD), for texture characterization in high resolution remote sensing (HRRS) image classification. We evaluated the relative performance of WLD against the well-known Local Binary Pattern (LBP) algorithm. Our experiments show WLD consistently provides more accurate results than LBP or pure variance (VAR) and that adding contrast information further improves its performance.