Alone versus In-a-group: A Comparative Analysis of Facial Affect Recognition

Automatic affect analysis and understanding has become a well established research area in the last two decades. Recent works have started moving from individual to group scenarios. However, little attention has been paid to comparing the affect expressed in individual and group settings. This paper presents a framework to investigate the differences in affect recognition models along arousal and valence dimensions in individual and group settings. We analyse how a model trained on data collected from an individual setting performs on test data collected from a group setting, and $\textit{vice versa}$. A third model combining data from both individual and group settings is also investigated. A set of experiments is conducted to predict the affiective states along both arousal and valence dimensions on two newly collected databases that contain sixteen participants watching affiective movie stimuli in individual and group settings, respectively. The experimental results show that (1) the affect model trained with group data performs better on individual test data than the model trained with individual data tested on group data, indicating that facial behaviours expressed in a group setting capture more variation than in an individual setting; and (2) the combined model does not show better performance than the affect model trained with a specific type of data (i.e., individual or group), but proves a good compromise. These results indicate that in settings where multiple affect models trained with different types of data are not available, using the affect model trained with group data is a viable solution

Your fellows matter: Affect analysis across subjects in group videos

Automatic affect analysis has become a well established research area in the last two decades. Recent works have started moving from individual to group scenarios. However, little attention has been paid to investigating how individuals in a group influence the affective states of each other. In this paper, we propose a novel framework for cross-subjects affect analysis in group videos. Specifically, we analyze the correlation of the affect among group members and investigate the automatic recognition of the affect of one subject using the behaviours expressed by another subject in the same group. A set of experiments are conducted using a recently collected database aimed at affect analysis in group settings. Our results show that (1) people in the same group do share more information in terms of behaviours and emotions than people in different groups; and (2) the affect of one subject in a group can be better predicted using the expressive behaviours of another subject within the same group than using that of a subject from a different group. This work is of great importance for affect recognition in group settings: when the information of one subject is unavailable due to occlusion, head/body poses etc., we can predict his/her affect by employing the expressive behaviours of the other subject(s).European Unions Horizon 202

Evaluation of vertebral artery dominance, hypoplasia and variations in the origin: angiographic study in 254 patients

Background: The aim of this study was to determine the dimensional characteristics and variations in the origin of vertebral arteries (VA). Materials and methods: We retrospectively reviewed angiographic studies in 254 patients (133 males, 121 females) for the evaluation of diameter differences in VA. We examined different criteria from the literature (difference of ≥ 0.3 mm, ≥ 0.8 mm, ≥ 1 mm between the widths of two VA and diameter ratio more than 1.4) to find out the dominant VA, rate of co-dominance and hypoplasia. The differences among groups were analysed using the c2 and Kruskal-Wallis test. Also concordance analysis test was used to determine correspondence between the tests. We also noticed the variations in the origin of VA. Results: The average diameter of VA in 254 patients was 3.21 ± 0.7 mm on the right, and 3.16 ± 0.7 mm on the left. The average diameter difference was found 0.88 ± 0.7 mm. The rate of hypoplasia was found 7.1% on the right and 9.4% on the left. Among 254 patients according to the criterion of any diameter difference; right side was found wider in 126 (49.6%) patients and left side was found wider in 120 (47.2%) patients. The criterion of 0.3 mm or greater difference showed right VA dominance in 107 (42.1%) patients, left VA dominance in 99 (39%) patients. Co-dominance was mainly observed when we used the criteria of 0.8 mm and 1 mm or greater difference and diameter ratio more than 1.4. We found out harmony of two criterion of difference of ≥ 0.8 mm and ≥ 1 mm (concordance analysis test, 76.1%). There was no statistically significant relation between age, gender and any dominance criteria (p > 0.05). The majority of VA showed classical origin arising from both subclavian arteries with a rate of 94.9%. Conclusions: The most striking result we have found is the dominance of the right VA in diameter by using all different criteria unlike with previous reports in the literature.

Investigation of Adhesion and Tribological Behavior of Borided AISI 310 Stainless Steel

In the present study, the effects of the boriding process on adhesion and tribological properties of AISI 310 steel were investigated. Boriding was performed in a solid medium consisting of Ekabor-II powders at 1123 and 1323K for 2 and 6 h. The boride layer was characterized by optical microscopy, the X-ray diffraction technique and the micro-Vickers hardness tester. The X-ray diffraction analysis of the boride layers on the surface of the steels revealed the existence of FexBy, CrxBy and NixBy compounds. Depending on the chemical composition of substrates, the boride layer thickness on the surface of the AISI 310 steel was found to be 56.74 μm. The hardness of the boride compounds formed on the surface of the AISI 310 steel ranged from 1658 to 2284 HV0,1, whereas the Vickers hardness value of the untreated steel AISI 310 was 276 HV0,1. The wear tests were carried out in a ball-disc arrangement under a dry friction condition at room temperature with an applied load of 10N and with a sliding speed of 0.3 m/s, at a sliding distance of 1000m. The wear surfaces of the steel were analyzed using an SEM microscopy and X-ray energy dispersive spectroscopy EDS. It was observed that the wear rate of unborided and borided AISI 310 steel ranged from 4.57 to 71.42 mm3/Nm

Fully Automatic Analysis of Engagement and Its Relationship to Personality in Human-Robot Interactions

Engagement is crucial to designing intelligent systems that can adapt to the characteristics of their users. This paper focuses on automatic analysis and classification of engagement based on humans’ and robot’s personality profiles in a triadic human-human-robot interaction setting. More explicitly, we present a study that involves two participants interacting with a humanoid robot, and investigate how participants’ personalities can be used together with the robot’s personality to predict the engagement state of each participant. The fully automatic system is firstly trained to predict the Big Five personality traits of each participant by extracting individual and interpersonal features from their nonverbal behavioural cues. Secondly, the output of the personality prediction system is used as an input to the engagement classification system. Thirdly, we focus on the concept of “group engagement”, which we define as the collective engagement of the participants with the robot, and analyse the impact of similar and dissimilar personalities on the engagement classification. Our experimental results show that (i) using the automatically predicted personality labels for engagement classification yields an F-measure on par with using the manually annotated personality labels, demonstrating the effectiveness of the automatic personality prediction module proposed; (ii) using the individual and interpersonal features without utilising personality information is not sufficient for engagement classification, instead incorporating the participants’ and robot’s personalities with individual/interpersonal features increases engagement classification performance; and (iii) the best classification performance is achieved when the participants and the robot are extroverted, while the worst results are obtained when all are introverted.This work was performed within the Labex SMART project (ANR-11-LABX-65) supported by French state funds managed by the ANR within the Investissements d’Avenir programme under reference ANR-11-IDEX-0004-02. The work of Oya Celiktutan and Hatice Gunes is also funded by the EPSRC under its IDEAS Factory Sandpits call on Digital Personhood (Grant Ref.: EP/L00416X/1).This is the author accepted manuscript. The final version is available from Institute of Electrical and Electronics Engineers via http://dx.doi.org/10.1109/ACCESS.2016.261452

A deep generic to specific recognition model for group membership analysis using non-verbal cues

Automatic understanding and analysis of groups has attracted increasing attention in the vision and multimedia communities in recent years. However, little attention has been paid to the automatic analysis of the non-verbal behaviors and how this can be utilized for analysis of group membership, i.e., recognizing which group each individual is part of. This paper presents a novel Support Vector Machine (SVM) based Deep Specific Recognition Model (DeepSRM) that is learned based on a generic recognition model. The generic recognition model refers to the model trained with data across different conditions, i.e., when people are watching movies of different types. Although the generic recognition model can provide a baseline for the recognition model trained for each specific condition, the different behaviors people exhibit in different conditions limit the recognition performance of the generic model. Therefore, the specific recognition model is proposed for each condition separately and built on the top of the generic recognition model. We conduct a set of experiments using a database collected to study group analysis while each group (i.e., four participants together) were watching a number of long movie segments. The proposed deep specific recognition model (44%) outperforms the generic recognition model (26%). The recognition of group membership also indicates that the non-verbal behaviors of individuals within a group share commonalities

Computational Analysis of Valence and Arousal in Virtual Reality Gaming using Lower Arm Electromyograms

Progress in the affective computing field has led to the creation of affect-aware games that aim to adapt to the emotions experienced by the players. In this paper we focus on affect recognition in virtual reality (VR) gaming, a problem that to the best of our knowledge has not yet been sufficiently explored. We aim to answer two research questions: (i) Is it possible to reliably capture and recognize the affective state of a person based on EMG sensors placed on their lower arms, while they interact with the virtual environment? and (ii) Is EMG signal from one arm sufficient for detecting affect? We conducted a study in which 8 people were playing a set of VR games with two EMG sensors placed on their arms. We analysed the EMG signals and extracted a number of features to infer the affective states of the players. Our experimental results show that the EMG measures from left and right arms provide sufficient information to detect emotions experienced by a player of a VR game. Our results also show that classifying a DWT-db1 signal with Support Vector Machine (SVM) yields F1=0.91 for predicting low/high arousal and F1=0.85 for predicting positive/negative valence when using just the left-arm EMG signal. To the best of our knowledge, this is the first work that uses EMG data from arm movements as a single source of affective information and addresses affect recognition in VR gaming.The work of H. Gunes is partially supported by the Innovate UK project Sensing Feeling (project no. 102547)

Exciting Complexity: The Role of Motor Circuit Elements in ALS Pathophysiology

Amyotrophic lateral sclerosis (ALS) is a fatal disease, characterized by the degeneration of both upper and lower motor neurons. Despite decades of research, we still to date lack a cure or disease modifying treatment, emphasizing the need for a much-improved insight into disease mechanisms and cell type vulnerability. Altered neuronal excitability is a common phenomenon reported in ALS patients, as well as in animal models of the disease, but the cellular and circuit processes involved, as well as the causal relevance of those observations to molecular alterations and final cell death, remain poorly understood. Here, we review evidence from clinical studies, cell type-specific electrophysiology, genetic manipulations and molecular characterizations in animal models and culture experiments, which argue for a causal involvement of complex alterations of structure, function and connectivity of different neuronal subtypes within the cortical and spinal cord motor circuitries. We also summarize the current knowledge regarding the detrimental role of astrocytes and reassess the frequently proposed hypothesis of glutamate-mediated excitotoxicity with respect to changes in neuronal excitability. Together, these findings suggest multifaceted cell type-, brain area- and disease stage- specific disturbances of the excitation/inhibition balance as a cardinal aspect of ALS pathophysiology

Registration-free Face-SSD: Single shot analysis of smiles, facial attributes, and affect in the wild

In this paper, we present a novel single shot face-related task analysis method, called Face-SSD, for detecting faces and for performing various face-related (classification/regression) tasks including smile recognition, face attribute prediction and valence-arousal estimation in the wild. Face-SSD uses a Fully Convolutional Neural Network (FCNN) to detect multiple faces of different sizes and recognise/regress one or more face-related classes. Face-SSD has two parallel branches that share the same low-level filters, one branch dealing with face detection and the other one with face analysis tasks. The outputs of both branches are spatially aligned heatmaps that are produced in parallel - therefore Face-SSD does not require that face detection, facial region extraction, size normalisation, and facial region processing are performed in subsequent steps. Our contributions are threefold: 1) Face-SSD is the first network to perform face analysis without relying on pre-processing such as face detection and registration in advance - Face-SSD is a simple and a single FCNN architecture simultaneously performing face detection and face-related task analysis - those are conventionally treated as separate consecutive tasks; 2) Face-SSD is a generalised architecture that is applicable for various face analysis tasks without modifying the network structure - this is in contrast to designing task-specific architectures; and 3) Face-SSD achieves real-time performance (21 FPS) even when detecting multiple faces and recognising multiple classes in a given image. Experimental results show that Face-SSD achieves state-of-the-art performance in various face analysis tasks by reaching a recognition accuracy of 95.76% for smile detection, 90.29% for attribute prediction, and Root Mean Square (RMS) error of 0.44 and 0.39 for valence and arousal estimation