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CFP: The OMG-Empathy Prediction Challenge

by Pablo Barros

CALL FOR PARTICIPATION The One-Minute Gradual-Empathy Prediction (OMG-Empathy) Competition held in partnership with the IEEE International Conference on Automatic Face and Gesture Recognition 2019 in Lille, France. https://www2.informatik.uni-hamburg.de/wtm/omgchallenges/omg_empathy.html I. Aim and Scope The ability to perceive, understand and respond to social interactions in a human-like manner is one of the most desired capabilities in artificial agents, particularly social robots. These skills are highly complex and require a focus on several different aspects of research, including affective understanding. An agent which is able to recognize, understand and, most importantly, adapt to different human affective behaviors can increase its own social capabilities by being able to interact and communicate in a natural way. Emotional expression perception and categorization are extremely popular in the affective computing community. However, the inclusion of emotions in the decision-making process of an agent is not considered in most of the research in this field. To treat emotion expressions as the final goal, although necessary, reduces the usability of such solutions in more complex scenarios. To create a general affective model to be used as a modulator for learning different cognitive tasks, such as modeling intrinsic motivation, creativity, dialog processing, grounded learning, and human-level communication, only emotion perception cannot be the pivotal focus. The integration of perception with intrinsic concepts of emotional understanding, such as a dynamic and evolving mood and affective memory, is required to model the necessary complexity of an interaction and realize adaptability in an agent's social behavior. Such models are most necessary for the development of real-world social systems, which would communicate and interact with humans in a natural way on a day-to-day basis. This could become the next goal for research on Human-Robot Interaction (HRI) and could be an essential part of the next generation of social robots. For this challenge, we designed, collected and annotated a novel corpus based on human-human interaction. This novel corpus builds on top of the experience we gathered while organizing the OMG-Emotion Recognition Challenge, making use of state-of-the-art frameworks for data collection and annotation. The One-Minute Gradual Empathy datasets (OMG-Empathy) contain multi-modal recordings of different individuals discussing predefined topics. One of them, the actor, shares a story about themselves while the other, the listener, reacts to it emotionally. We annotated each interaction based on the listener's own assessment of how they felt while the interaction was taking place. We encourage the participants to propose state-of-the-art solutions not only based on deep, recurrent and self-organizing neural networks but also traditional methods for feature representation and data processing. We also enforce that the use of contextual information, as well as personalized solutions for empathy assessment, will be extremely important for the development of competitive solutions. II. Competition Tracks We let available for the challenge a pre-defined set of training, validation and testing samples. We separate our samples based on each story: 4 stories for training, 1 for validation and 3 for testing. Each story sample is composed of 10 videos with interactions, one for each listener. Although using the same training, validation and testing data split, we propose two tracks which will measure different aspects of the self-assessed empathy: The Personalized Empathy track, where each team must predict the empathy of a specific person. We will evaluate the ability of proposed models to learn the empathic behavior of each of the subjects over a newly perceived story. We encourage the teams to develop models which take into consideration the individual behavior of each subject in the training data. The Generalized Empathy track, where the teams must predict the general behavior of all the participants over each story. We will measure the performance of the proposed models to learn a general empathic measure for each of the stories individually. We encourage the proposed models to take into consideration the aggregated behavior of all the participants for each story and to generalize this behavior in a newly perceived story. The training and validation samples will be given to the participants at the beginning of the challenge together with all the associated labels. The test set will be given to the participants without the associated labels. The team`s predictions on the test set will be used to calculate the final metrics of the challenge. III. How to Participate To participate to the challenge, please send us an email to barros @ informatik.uni-hamburg.de with the title "OMG-Empathy Team Registration". This e-mail must contain the following information: Team Name Team Members Affiliation Participating tracks We split the corpus into three subsets: training, validation and testing. The participants will receive the training and validation sets, together with the associated annotations once they subscribe to the challenge. The subscription will be done via e-mail. Each participant team must consist of 1 to 5 participants and must agree to use the data only for scientific purposes. Each team can choose to take part in one or both the tracks. After the training period is over, the testing set will be released without the associated annotations. Each team must submit, via e-mail, their final predictions as a .csv file for each video on the test set. Together with the final submission, each team must send a short 2-4 pages paper describing their solution published on Arxiv and the link for a github page to their solution. If a team fails to submit any of these items, their submission will be invalidated. Each team can submit 3 complete submissions for each track. IV. Important Dates 25th of September 2018 - Opening of the Challenge - Team registrations begin 1st of October 2018 - Training/validation data and annotation available 1st of December 2018 - Test data release 3rd of December 2018 - Final submission (Results and code) 5th of December 2018 - Final submission (Paper) 7th of December 2018 - Announcement of the winners V. Organization Pablo Barros, University of Hamburg, Germany Nikhil Churamani, University of Cambridge, United Kingdom Angelica Lim, Simon Fraser University, Canada Stefan Wermter, Hamburg University, Germany -- Dr.rer.nat. Pablo Barros Postdoctoral Research Associate - Crossmodal Learning Project (CML) Knowledge Technology Department of Informatics University of Hamburg Vogt-Koelln-Str. 30 22527 Hamburg, Germany Phone: +49 40 42883 2535 Fax: +49 40 42883 2515 barros at informatik.uni-hamburg.de https://www.inf.uni-hamburg.de/en/inst/ab/wtm/people/barros.html https://www.inf.uni-hamburg.de/en/inst/ab/wtm/

7 years, 1 month

Call for abstracts: special session on human health monitoring based on computer vision, IEEE International Conference on Automatic Face and Gesture Recognition (FG’19), Lille, France, May 14-18, 2019.

by ABHIJIT DAS

Dear colleagues, We are inviting abstract submissions for a special session on “Human Health Monitoring Based on Computer Vision”, as part of the 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG’19, http://fg2019.org/), Lille, France, May 14-18, 2019. Details on the special session follow below. Title, abstract, list of authors, as well as the name of the corresponding author, should be emailed directly to Abhijit Das (abhijitdas2048(a)gmail.com). We hope to receive abstracts before Thursday, September 27th. Feel free to contact Abhijit Das if you have any further questions. Kindly circulate this email to others who might be interested. We look forward to your contributions! François Brémond (INRIA, France) Antitza Dantcheva (INRIA, France) Abhijit Das (INRIA, France) Xilin Chen (CAS, China) Hu Han (CAS, China) -------------------------------------------------------------------------------------------- *Call for abstract for FG 2018 special session * *on* *Human Health Monitoring Based on Computer Vision* --------------------------------------------------------------- Human Health Monitoring Based on Computer Vision has gained rapid scientific growth in the last years, with many research articles and complete systems based on a set of features, extracted from face and gesture. Researchers from the computer, as well as from medical science have granted significant attention, with goals ranging from patient analysis and monitoring to diagnostics. (e.g., for dementia, depression, healthcare, physiological measurement [5, 6]). Despite the progress, there are various open, unexplored, and unidentified challenges. Such as the robustness of these techniques in the real-world scenario, collecting large dataset for research, heterogeneity of the acquiring environment and the artefacts. Moreover, healthcare represents an area of broad economic (e.g., https://www.prnewswire.com/news-releases/healthcare-automation-market-to-re…), social, and scientific impact. Therefore, it is imperative to foster efforts coming from computer vision, machine learning, and the medical domain, as well as multidisciplinary efforts. Towards this, we propose a special session, with a focus on multidisciplinary efforts. We aim to document recent advancements in automated healthcare, as well as enable and discuss progress.. Therefore, the goal of this special session is to bring together researchers and practitioners working in this area of computer vision and medical science, and to address a wide range of theoretical and practical issues related to real-life healthcare systems. Topics of interest include, but are not limited to: · Health monitoring based on face analysis, · Health monitoring based on gesture analysis, · Health monitoring based corporeal-based visual features, · Depression analysis based on visual features, · Face analytics for human behaviour understanding, · Anxiety diagnosis based on face and gesture · Physiological measurement employing face analytics, · Databases on health monitoring, e.g., depression analysis, · Augmentative and alternative communication, · Human-robot interaction, · Home healthcare, · Technology for cognition, · Automatic emotional hearing and understanding, · Visual attention and visual saliency, · Assistive living, · Privacy preserving systems, · Quality of life technologies, · Mobile and wearable systems, · Applications for the visually impaired, · Sign language recognition and applications for hearing impaired, · Applications for the ageing society, · Personalized monitoring, · Egocentric and first-person vision, · Applications to improve the health and wellbeing of children and the elderly, etc. In addition, we plan to organise a special issue in a journal with the extended version of accepted special session papers.

7 years, 1 month

Resources for Gestures and Emotion Recognition

by Pablo Barros

Dear all, We are very happy to announce the release of resource material related to our research on affective computing and gestures recognition. This resource contains research on hand gesture and emotion processing (auditory, visual and crossmodal) recognition and processing, organized as three datasets (NCD, GRIT, and OMG-Emotion), source code for proposed neural network solutions, pre-trained models, and ready-to-run demos. The NAO Camera hand posture Database (NCD) was designed and recorded using the camera of a NAO robot and contains four different hand postures. A total of 2000 images were recorded. In each image, the hand was is present in different positions, not always in the centralized, and sometimes with occlusion of some fingers. The Gesture Commands for Robot InTeraction (GRIT) dataset contains recordings of six different subjects performing eight command gestures for Human-Robot Interaction (HRI): Abort, Circle, Hello, No, Stop, Turn Right, Turn Left, and Warn. We recorded a total of 543 sequences with a varying number of frames in each one. The One-Minute Gradual Emotion Corpus (OMG-Emotion) is composed of Youtube videos which are about a minute in length and are annotated taking into consideration a continuous emotional behavior. The videos were selected using a crawler technique that uses specific keywords based on long-term emotional behaviors such as "monologues", "auditions", "dialogues" and "emotional scenes". After the videos were selected, we created an algorithm to identify weather the video had at least two different modalities which contribute for the emotional categorization: facial expressions, language context, and a reasonably noiseless environment. We selected a total of 420 videos, totaling around 10 hours of data. Together with the datasets, we provide the source code for different proposed neural models. These models are based on novel deep and self-organizing neural networks which deploy different mechanisms inspired by neuropsychological concepts. All of our models are formally described in different high-impact peer-reviewed publications. We also provide a ready-to-run demo for visual emotion recognition based on our proposed models. These resources are accessible through our GitHub link: https://github.com/knowledgetechnologyuhh/EmotionRecognitionBarros . We hope that with these resources we can contribute to the areas of affective computing and gesture recognition and foster the development of innovative solutions. -- Dr.rer.nat. Pablo Barros Postdoctoral Research Associate - Crossmodal Learning Project (CML) Knowledge Technology Department of Informatics University of Hamburg Vogt-Koelln-Str. 30 22527 Hamburg, Germany Phone: +49 40 42883 2535 Fax: +49 40 42883 2515 barros at informatik.uni-hamburg.de https://www.inf.uni-hamburg.de/en/inst/ab/wtm/people/barros.html https://www.inf.uni-hamburg.de/en/inst/ab/wtm/

7 years, 2 months

Open Positions: 1 Ph.D. student and 2 Postdocs in the area of Computer Vision and Deep Learning at INRIA Sophia Antipolis, France

by Antitza Dantcheva

Open Positions: 1 Ph.D. student and 2 Postdocs in the area of Computer Vision and Deep Learning at INRIA Sophia Antipolis, France ------------------------------ ------------------------------ ------------------------------ ------------------------------ ------------------------------ ---------------------- Positions are offered within the frameworks of the prestigious grants - ANR JCJC Grant *ENVISION*: "Computer Vision for Automated Holistic Analysis for Humans" and the - INRIA - CAS grant *FER4HM* "Facial expression recognition with application in health monitoring" and are ideally located in the heart of the French Riviera, inside the multi-cultural silicon valley of Europe. Full announcements: - Open Ph.D.-Position in Computer Vision / Deep Learning (M/F) *ENVISION*: http://antitza.com/ANR_phd.pdf - Open Post Doc - Position in Computer Vision / Deep Learning (M/F) *FER4HM*: http://antitza.com/INRIA_CAS_p ostdoc.pdf - Open Post Doc - Position in Computer Vision / Deep Learning (M/F) (advanced level) *ENVISION*: http://antitza.com/ANR_postdoc .pdf To apply, please email a full application to Antitza Dantcheva ( antitza.dantcheva(a)inria.fr ), indicating the position in the e-mail subject line.

7 years, 2 months

Mooney face data base online

by Caspar M. Schwiedrzik

Dear all, we just published a database of 500 Mooney face stimuli for visual perception research. Please find the paper here: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0200106 Each face was tested in a cohort of healthy adults for face detection difficulty and inversion effects. We also provide a comparison with Craig Mooney's original stimulus set. The stimuli and accompanying data are available from Figshare (https://figshare.com/account/articles/5783037) under a CC BY license. Please feel free to use them for your research. Caspar

7 years, 3 months

Lab Manager/Research Assistant position available at the University of Richmond

by BURNS E. (597993)

RESEARCH SPECIALIST POSITION AT COGNITIVE NEUROSCIENCE LAB, University of Richmond The Cognitive Neuroscience laboratory of Dr. Cindy Bukach is seeking a highly organized and energetic person to serve as full-time research specialist. The lab conducts research on object and face recognition in cognitively intact and impaired individuals, using electrophysiology (EEG and ERP) and behavioral methods. The duties of the research specialist include: Conducts Cognitive Neuroscience research on human subjects using both behavioral and ERP methods, including programming, recruiting, testing and statistical analysis. Under limited supervision, coordinates and supervises student research activities, ensuring adherence to safety and ethical regulations related to research with human subjects. Performs administrative duties such as database management, scheduling, hardware/software maintenance, website maintenance, equipment maintenance and general faculty support. EDUCATION & EXPERIENCE: Bachelor’s degree or equivalent in psychology, neuroscience, cognitive science, computer science or related field 2 years experience in a research lab (preferably in cognitive or cognitive neuroscience laboratory using Event-related potential method) PREFERRED QUALIFICATIONS (any of the following highly desired): Prior research experience with Event-related potential method and data analysis Advanced computer skills (Matlab, Python, Java, etc.) For more information, please contact Cindy Bukach at cbukach(a)richmond.edu<mailto:cbukach@richmond.edu> Cindy M. Bukach, PhD Chair, Department of Psychology Associate Professor of Cognitive Neuroscience MacEldin Trawick Endowed Professor of Psychology 209 Richmond Hall 28 Westhampton Way University of Richmond, Virginia 23173 Phone: (804) 287-6830 Fax: (804) 287-1905

7 years, 4 months

PhD scholarship in face identification at UNSW Sydney

by David White

Call for Expressions of Interest: UNSW Scientia PhD Scholarship "Understanding super-recognition to improve face identification systems" The UNSW Forensic Psychology Group invites Expressions of Interest for a unique PhD scholarship opportunity. UNSW Scientia PhD scholars are awarded $50k per year, comprising a tax-free living allowance of $40k per year for 4 years, and a support package of up to $10k per year to provide financial support for career development activities. The project is targeted at PhD candidates that are qualified to honours and/or masters level in psychology, computer science of cognitive science. We are particularly interested to hear from applicants with work experience in research, government or industry. The topic of this thesis is open to negotiation, but we hope that the work can contribute to our broader goal of understanding how the best available human and machine solutions to face identification can be combined to produce optimal systems. For more details and to apply see the following links: https://www.2025.unsw.edu.au/apply/scientia-phd-scholarships/understanding-… http://forensic.psy.unsw.edu.au/joinus.html https://www.2025.unsw.edu.au/apply/<https://www.2025.unsw.edu.au/apply/scientia-phd-scholarships/understanding-…> Please direct informal inquiries to david.white(a)unsw.edu.au<mailto:david.white@unsw.edu.au>

7 years, 4 months

Postdoc positions @ U Glasgow

by Rachael Jack

Dear colleagues – please circulate widely! We are delighted to announce the following positions at the Institute of Neuroscience & Psychology, University of Glasgow, Scotland. All positions are funded by the ERC project Computing the Face Syntax of Social Communication led by Dr. Rachael Jack. 2 X Postdoctoral Researcher (5 years) Ref: 021275 The post-holders will contribute to a large 5-year research project to model culture-specific dynamic social face signals with transference to social robotics. The post requires expert knowledge in all or a substantial subset of the following: vision science, psychophysics, social perception, cultural psychology, dynamic social face signalling, human-robot interaction, reverse correlation techniques, MATLAB programming, computational methods, and designing behavioral experiments. Informal enquiries may be made to Dr Rachael Jack Email: Rachael.Jack(a)glasgow.ac.uk<mailto:Rachael.Jack@glasgow.ac.uk> Apply online at: www.glasgow.ac.uk/jobs<http://www.glasgow.ac.uk/jobs> Closing date: 12 June 2018<x-apple-data-detectors://2> See also http://www.jobs.ac.uk/job/BJU668/research-assistant-associate/ The University has recently been awarded the Athena SWAN Institutional Bronze Award. The University is committed to equality of opportunity in employment. The University of Glasgow, charity number SC004401. Dr. Rachael E. Jack, Ph.D. Lecturer Institute of Neuroscience & Psychology School of Psychology University of Glasgow +44 (0) 141 5087<tel:+44%20141%205087> www.psy.gla.ac.uk/schools/psychology/staff/rachaeljack/<http://www.psy.gla.ac.uk/schools/psychology/staff/rachaeljack/> [University of Glasgow: The Times Scottish University of the Year 2018]

7 years, 5 months

OMG- Emotion Recognition Challenge - Final Results

by Pablo Barros

Dear all, I would like to share with you all the results of our first OMG- Emotion Recognition Challenge. Our challenge is based on the One-Minute-Gradual Emotion Dataset (OMG-Emotion Dataset), which is composed of 567 emotion videos with an average length of 1 minute, collected from a variety of Youtube channels. Each team had a task to describe each video with a continuous space of arousal/valence domain. The challenge had a total of 34 teams registered, from which we got 11 final submissions. Each final submission was composed of a short paper describing the solution and the link to the code repository. The solutions used different modalities (ranging from unimodal audio and vision to multimodal audio, vision, and text), and thus provide us with a very complex evaluation scenario. All the submissions were based on neural network models. We split results into arousal and valence. For arousal, the best results came from the GammaLab team. Their three submissions are our top 3 CCC arousal, followed by the three submissions from the audEERING team, and the two submissions from the HKUST-NISL2018 team. For valence, the GammaLab team stays still in first (with their three submissions), followed by the two submissions of ADSC team and the three submissions from the iBug team. Congratulations to you all! We provide a leaderboard on our website ( https://www2.informatik.uni-hamburg.de/wtm/OMG-EmotionChallenge/ ), which will be permanently stored. This way, everyone can see the final results of the challenge, have a quick access to a formal description of the solutions and to the codes. This will help to disseminate knowledge even further and will improve the reproducibility of your solutions. We also provide a general leaderboard which will be updated constantly with new submissions. If you are interested in having your score in our general leaderboard, just send us an e-mail following the instructions on our website. I would also to invite you all to the presentation of the challenge summary during the WCCI/IJCNN 2018 in Rio de Janeiro, Brasil. Best Regards, Pablo -- Dr. Pablo Barros Postdoctoral Research Associate - Crossmodal Learning Project (CML) Knowledge Technology Department of Informatics University of Hamburg Vogt-Koelln-Str. 30 22527 Hamburg, Germany Phone: +49 40 42883 2535 Fax: +49 40 42883 2515 barros at informatik.uni-hamburg.de https://www.inf.uni-hamburg.de/en/inst/ab/wtm/people/barros.html https://www.inf.uni-hamburg.de/en/inst/ab/wtm/

7 years, 5 months

Research Associate

by Psychology-Admin

Please see attached an advert for a Research Associate position working with Dr Rachael Jack within the School of Psychology/Institute of Neuroscience & Psychology. Informal enquiries can be directed to Rachael Email: Rachael.Jack(a)glasgow.ac.uk<mailto:Rachael.Jack@glasgow.ac.uk> [University of Glasgow: The Times Scottish University of the Year 2018]

7 years, 5 months

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