Overview of the Multimedia Information Processing for Personality and Social Networks Analysis Contest

International audienceProgress in the autonomous analysis of human behavior from multimodal information has lead to very effective methods able to deal with problems like action/gesture/activity recognition, pose estimation, opinion mining, user tailored retrieval, etc. However, it is only recently that the community has been starting to look into related problems associated with more complex behavior, including personality analysis, deception detection, among others. We organized an academic contest co-located with ICPR2018 running two tasks in this direction. On the one hand, we organized an information fusion task in the context of multi-modal image retrieval in social media. On the other hand, we ran another task in which we aim to infer personality traits from written essays, including textual and handwritten information. This paper describes both tasks, detailing for each of them the associated problem, data sets, evaluation metrics and protocol, as well as an analysis of the performance of simple baselines

ChaLearn LAP 2016: First Round Challenge on First Impressions - Dataset and Results

This paper summarizes the ChaLearn Looking at People 2016 First Impressions challenge data and results obtained by the teams in the first round of the competition. The goal of the competition was to automatically evaluate five “apparent” personality traits (the so-called “Big Five”) from videos of subjects speaking in front of a camera, by using human judgment. In this edition of the ChaLearn challenge, a novel data set consisting of 10,000 shorts clips from YouTube videos has been made publicly available. The ground truth for personality traits was obtained from workers of Amazon Mechanical Turk (AMT). To alleviate calibration problems between workers, we used pairwise comparisons between videos, and variable levels were reconstructed by fitting a Bradley-Terry-Luce model with maximum likelihood. The CodaLab open source platform was used for submission of predictions and scoring. The competition attracted, over a period of 2 months, 84 participants who are grouped in several teams. Nine teams entered the final phase. Despite the difficulty of the task, the teams made great advances in this round of the challenge

Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications

The last decade has seen a revolution in the theory and application of machine learning and pattern recognition. Through these advancements, variable ranking has emerged as an active and growing research area and it is now beginning to be applied to many new problems. The rationale behind this fact is that many pattern recognition problems are by nature ranking problems. The main objective of a ranking algorithm is to sort objects according to some criteria, so that, the most relevant items will appear early in the produced result list. Ranking methods can be analyzed from two different methodological perspectives: ranking to learn and learning to rank. The former aims at studying methods and techniques to sort objects for improving the accuracy of a machine learning model. Enhancing a model performance can be challenging at times. For example, in pattern classification tasks, different data representations can complicate and hide the different explanatory factors of variation behind the data. In particular, hand-crafted features contain many cues that are either redundant or irrelevant, which turn out to reduce the overall accuracy of the classifier. In such a case feature selection is used, that, by producing ranked lists of features, helps to filter out the unwanted information. Moreover, in real-time systems (e.g., visual trackers) ranking approaches are used as optimization procedures which improve the robustness of the system that deals with the high variability of the image streams that change over time. The other way around, learning to rank is necessary in the construction of ranking models for information retrieval, biometric authentication, re-identification, and recommender systems. In this context, the ranking model's purpose is to sort objects according to their degrees of relevance, importance, or preference as defined in the specific application.Comment: European PhD Thesis. arXiv admin note: text overlap with arXiv:1601.06615, arXiv:1505.06821, arXiv:1704.02665 by other author

Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications

The last decade has seen a revolution in the theory and application of machine learning and pattern recognition. Through these advancements, variable ranking has emerged as an active and growing research area and it is now beginning to be applied to many new problems. The rationale behind this fact is that many pattern recognition problems are by nature ranking problems. The main objective of a ranking algorithm is to sort objects according to some criteria, so that, the most relevant items will appear early in the produced result list. Ranking methods can be analyzed from two different methodological perspectives: ranking to learn and learning to rank. The former aims at studying methods and techniques to sort objects for improving the accuracy of a machine learning model. Enhancing a model performance can be challenging at times. For example, in pattern classification tasks, different data representations can complicate and hide the different explanatory factors of variation behind the data. In particular, hand-crafted features contain many cues that are either redundant or irrelevant, which turn out to reduce the overall accuracy of the classifier. In such a case feature selection is used, that, by producing ranked lists of features, helps to filter out the unwanted information. Moreover, in real-time systems (e.g., visual trackers) ranking approaches are used as optimization procedures which improve the robustness of the system that deals with the high variability of the image streams that change over time. The other way around, learning to rank is necessary in the construction of ranking models for information retrieval, biometric authentication, re-identification, and recommender systems. In this context, the ranking model's purpose is to sort objects according to their degrees of relevance, importance, or preference as defined in the specific application.Comment: European PhD Thesis. arXiv admin note: text overlap with arXiv:1601.06615, arXiv:1505.06821, arXiv:1704.02665 by other author

Assessment of ethnic and gender bias in automated first impression analysis

This thesis aims to investigate possible gender and ethnic biases in state-of-the-art deep learning methods in first impression analysis. Analysing a person with some software, businesses want to find the best candidate, without the person being judged by their gender or ethnicity. To achieve this, a first impression dataset about the big five personality traits, with additional information about the person’s gender and ethnic background, was used. Biases were both investigated with models trained on balanced and imbalanced data, where balanced here refers to the number of frames used from people classified as Asian, African-American, or Caucasian in the dataset. The results with both the balanced and imbalanced datasets were similar. With all the models the accuracy for Asians was much higher compared to others, which may come from the fact that the dataset did not include enough variance in the Asian data, so when evaluating, all Asians were seen similarly

On The Mind's Foreign Shores: The Origins of Henry A. Murray's Personology

Henry A. Murray (1893-1988) became one of America’s premier scholars in personality research. While most psychologists remember him as the co-developer of the Thematic Apperception Test, he and a large and devoted staff at the Harvard Psychological Clinic devised numerous techniques for studying personality, in support of a theory that Murray called personology (Morgan & Murray, 1935; Robinson, 1992). Personology was described at length in Murray’s first major work, Explorations in Personality (Murray, 1938). An amalgam of Jungian analysis and trait psychology, Murray obviously borrowed from a number of theoretical sources, including Gordon Allport (1967) and Kurt Lewin (1936, 1937/1999). A study of the origins of personology will contribute to a better understanding of the early years of personality psychology, including the limits of methodologies used in the 1930s. The term “individual differences” was not in vogue with Murray and his circle, but his system addressed a subject’s unique needs and external pressures. Since much of Murray’s original documentation has been archived at Harvard, the story of how Murray and his colleagues communicated and conceptualized their work may now be told. Questions remain about Murray’s specific influences. Murray (1959a, 1967) credited medicine and literature for inspiring personology, he later confessed an almost exclusive debt to his colleague and mistress, Christiana Morgan (Anderson, 1999; Douglas, 1993; Robinson, 1992). If a researcher looks beyond Murray’s brief autobiography (Murray, 1967) or the Robinson (1992) biography, which was primarily based on interviews with Murray, it is possible to find other roots to personology. The Henry A. Murray Papers in the Harvard University Archives offer extensive materials, most of which have not been previously used. Murray had close friendships with three senior scholars: mathematician Alfred North Whitehead, physician George Draper and biochemist Lawrence Henderson, and his notes and correspondence suggest that all three played a subtle but important role in establishing the foundations of personology. Previous Murray scholarship focused on Morgan and Carl Jung, but the importance of patterns and personology’s incorporation of evolution came from these now-obscure figures.Educational Psychology, Department o

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

The Allure of Celebrities: Unpacking Their Polysemic Consumer Appeal

The file attached to this record is the author's final peer reviewed version.To explain their deep resonance with consumers this paper unpacks the individual constituents of a celebrity’s polysemic appeal. While celebrities are traditionally theorised as unidimensional ‘semiotic receptacles of cultural meaning’, we conceptualise them here instead as human beings/performers with a multi-constitutional, polysemic consumer appeal. Supporting evidence is drawn from autoethnographic data collected over a total period of 25 months and structured through a hermeneutic analysis. In ‘rehumanising’ the celebrity, the study finds that each celebrity offers the individual consumer a unique and very personal parasocial appeal as a) the performer, b) the ‘private’ person behind the public performer, c) the tangible manifestation of either through products, and d) the social link to other consumers. The stronger these constituents, individually or symbiotically, appeal to the consumer’s personal desires the more s/he feels emotionally attached to this particular celebrity. Although using autoethnography means that the breadth of collected data is limited, the depth of insight this approach garners sufficiently unpacks the polysemic appeal of celebrities to consumers. The findings encourage talent agents, publicists and marketing managers to reconsider underlying assumptions in their talent management and/or celebrity endorsement practices. While prior research on celebrity appeal has tended to enshrine celebrities in a “dehumanised” structuralist semiosis, which erases the very idea of individualised consumer meanings, this paper reveals the multi-constitutional polysemy of any particular celebrity’s personal appeal as a performer and human being to any particular consumer