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Given the prevalence of depression worldwide and its major impact on society, several studies employed artificial intelligence modelling to automatically detect and assess depression. However, interpretation of these models and cues are rarely discussed in detail in the AI community, but have received increased attention lately. In this study, we aim to analyse the commonly selected features using a proposed framework of several feature selection methods and their effect on the classification results, which will provide an interpretation of the depression detection model. The developed framework aggregates and selects the most promising features for modelling depression detection from 38 feature selection algorithms of different categories. Using three real-world depression datasets, 902 behavioural cues were extracted from speech behaviour, speech prosody, eye movement and head pose. To verify the generalisability of the proposed framework, we applied the entire process to depression datasets individually and when combined. The results from the proposed framework showed that speech behaviour features (e.g. pauses) are the most distinctive features of the depression detection model. From the speech prosody modality, the strongest feature groups were F0, HNR, formants, and MFCC, while for the eye activity modality they were left-right eye movement and gaze direction, and for the head modality it was yaw head movement. Modelling depression detection using the selected features (even though there are only 9 features) outperformed using all features in all the individual and combined datasets. Our feature selection framework did not only provide an interpretation of the model, but was also able to produce a higher accuracy of depression detection with a small number of features in varied datasets. This could help to reduce the processing time needed to extract features and creating the model.

Many classical postural sway measures are based on linear analysis of different temporal characteristics of the body’s center of pressure (CoP). In some of the classical sway analysis methods, the anterior–posterior and the mediolateral aspects are analyzed separately. While these classical measures are found to be affected by aging, they cover different aspects of stability. Moreover, linear postural sway analysis is not efficient due to complexity of the human physiological functions. This study developed a single sway index based on the nonlinear analysis of the multidimensional CoP data and compared it in different participant age groups in different standing tests. The sway index performance was compared with six classical sway measures and six universal sway parameters. In all, 17 younger active lifestyle participants (31 ± 5.7), 12 participants age between 50 and 60 years (56.2 ± 3.3), and 32 older participants age 60 years and above (69.6 ± 6.2) were recruited for this study. Participants were asked to undergo three standing tests of double stance, single stance, and tandem stance all with eyes closed for 20 s. Using a global machine-learnt Gaussian mixture model, the multidimensional CoP data were clustered, and consequently an index was derived based on the results. Most classical and universal sway measures in the single stance and tandem stance were found to be significantly different in younger participants compared with the older ones. Our proposed sway index was significantly different in younger participants compared with 50–60 years participants in addition to older participants. The sway index also outperformed all classical and universal sway measures in the single and tandem stance tests with the sensitivity of 90.9% and 87.5%, the specificity of 82.4% and 84.3%, and AUC of 0.90 (95% CI, 0.81–1) and 0.91 (95% CI, 0.82–0.99), respectively. The findings demonstrated a strong potential of the sway index to be used as a single yet effective sway measurement.

Deep neural networks can easily be fooled by an adversary with minuscule perturbations added to an input image. The existing defense techniques suffer greatly under white-box attack settings, where an adversary has full knowledge of the network and can iterate several times to find strong perturbations. We observe that the main reason for the existence of such vulnerabilities is the close proximity of different class samples in the learned feature space of deep models. This allows the model decisions to be completely changed by adding an imperceptible perturbation to the inputs. To counter this, we propose to class-wise disentangle the intermediate feature representations of deep networks, specifically forcing the features for each class to lie inside a convex polytope that is maximally separated from the polytopes of other classes. In this manner, the network is forced to learn distinct and distant decision regions for each class. We observe that this simple constraint on the features greatly enhances the robustness of learned models, even against the strongest white-box attacks, without degrading the classification performance on clean images. We report extensive evaluations in both black-box and white-box attack scenarios and show significant gains in comparison to state-of-the-art defenses.

Visual tracking of multiple persons simultaneously is an important tool for group behaviour analysis. In this paper, we demonstrate that multi-target tracking in a network of non-overlapping cameras can be formulated in a framework, where the association among all given target hypotheses both within and between cameras is performed simultaneously. Our approach helps to overcome the fragility of multi-camera-based tracking, where the performance relies on the single-camera tracking results obtained at input level. In particular, we formulate an estimation of the target states as a multi-state graph optimization problem, in which the likelihood of each target hypothesis belonging to different identities is modeled. In addition, we learn the target-specific model to improve the similarity measure among targets based on the appearance cues. We also handle the occluded targets when there is no reliable evidence for the target’s presence and each target trajectory is expected to be fragmented into multiple tracks. An iterative procedure is proposed to solve the optimization problem, resulting in final trajectories that reveal the true states of the targets. The performance of the proposed approach has been extensively evaluated on challenging multi-camera non-overlapping tracking data sets, in which many difficulties, such as occlusion, viewpoint, and illumination variation, are present. The results of systematic experiments conducted on a large set of sequences show that the proposed approach outperforms several state-of-the-art trackers.

An estimated 350 million people worldwide are affected by depression. Using affective sensing technology, our long-term goal is to develop an objective multimodal system that augments clinical opinion during the diagnosis and monitoring of clinical depression. This paper steps towards developing a classification system-oriented approach, where feature selection, classification and fusion-based experiments are conducted to infer which types of behaviour (verbal and nonverbal) and behaviour combinations can best discriminate between depression and non-depression. Using statistical features extracted from speaking behaviour, eye activity, and head pose, we characterise the behaviour associated with major depression and examine the performance of the classification of individual modalities and when fused. Using a real-world, clinically validated dataset of 30 severely depressed patients and 30 healthy control subjects, a Support Vector Machine is used for classification with several feature selection techniques. Given the statistical nature of the extracted features, feature selection based on T-tests performed better than other methods. Individual modality classification results were considerably higher than chance level (83 percent for speech, 73 percent for eye, and 63 percent for head). Fusing all modalities shows a remarkable improvement compared to unimodal systems, which demonstrates the complementary nature of the modalities. Among the different fusion approaches used here, feature fusion performed best with up to 88 percent average accuracy. We believe that is due to the compatible nature of the extracted statistical features.

The recent advancement of social media has given users a platform to socially engage and interact with a larger population. Millions of images and videos are being uploaded everyday by users on the web from different events and social gatherings. There is an increasing interest in designing systems capable of understanding human manifestations of emotional attributes and affective displays. As images and videos from social events generally contain multiple subjects, it is an essential step to study these groups of people. In this paper, we study the problem of happiness intensity analysis of a group of people in an image using facial expression analysis. A user perception study is conducted to understand various attributes, which affect a person’s perception of the happiness intensity of a group. We identify the challenges in developing an automatic mood analysis system and propose three models based on the attributes in the study. An ‘in the wild’ image-based database is collected. To validate the methods, both quantitative and qualitative experiments are performed and applied to the problem of shot selection, event summarisation and album creation. The experiments show that the global and local attributes defined in the paper provide useful information for theme expression analysis, with results close to human perception results.

The issue of transferring facial performance from one person's face to another's has been an area of interest for the movie industry and the computer graphics community for quite some time. In recent years, deformable face models, such as the Active Appearance Model (AAM), have made it possible to track and synthesize faces in real time. Not surprisingly, deformable face model-based approaches for facial performance transfer have gained tremendous interest in the computer vision and graphics community. In this paper, we focus on the problem of real-time facial performance transfer using the AAM framework. We propose a novel approach of learning the mapping between the parameters of two completely independent AAMs, using them to facilitate the facial performance transfer in a more realistic manner than previous approaches. The main advantage of modeling this parametric correspondence is that it allows a "meaningful" transfer of both the nonrigid shape and texture across faces irrespective of the speakers' gender, shape, and size of the faces, and illumination conditions. We explore linear and nonlinear methods for modeling the parametric correspondence between the AAMs and show that the sparse linear regression method performs the best. Moreover, we show the utility of the proposed framework for a cross-language facial performance transfer that is an area of interest for the movie dubbing industry.

Two large facial-expression databases depicting challenging real-world conditions were constructed using a semi-automatic approach via a recommender system based on subtitles.