People spend considerable effort managing the impressions they give others. Social psychologists have shown that people manage these impressions differently depending upon their personality. Facebook and other social media provide a new forum for this fundamental process; hence, understanding people's behaviour on social media could provide interesting insights on their personality. In this paper we investigate automatic personality recognition from Facebook profile pictures. We analyze the effectiveness of four families of visual features and we discuss some human interpretable patterns that explain the personality traits of the individuals. For example, extroverts and agreeable individuals tend to have warm colored pictures and to exhibit many faces in their portraits, mirroring their inclination to socialize; while neurotic ones have a prevalence of pictures of indoor places. Then, we propose a classification approach to automatically recognize personality traits from these visual features. Finally, we compare the performance of our classification approach to the one obtained by human raters and we show that computer-based classifications are significantly more accurate than averaged human-based classifications for Extraversion and Neuroticism.
In the last years, the interest in automatic personality recognition has grown (see Vinciarelli and Mohammadi for a comprehensive survey @cite_0 ) and several works have started exploiting the wealth of data made available by social media @cite_41 @cite_22 @cite_44 @cite_12 , microphones and cameras @cite_39 @cite_59 @cite_43 @cite_33 , and mobile phones @cite_8 @cite_1 . Two works addressed the automatic recognition of personality traits from self-presentation videos @cite_13 @cite_37 . Biel @cite_13 used a dataset of 442 vlogs and asked external observers to rate vlogger's personality types; instead Batrinca @cite_55 recorded video self-presentations of 89 subjects in a lab setting, asking them to complete a self-assessed personality test.
@cite_37 @cite_22 @cite_33 @cite_8 @cite_41 @cite_55 @cite_1 @cite_39 @cite_0 @cite_44 @cite_43 @cite_59 @cite_13 @cite_12
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Despite an increasing interest in understanding human perception in social media through the automatic analysis of users' personality, existing attempts have explored user profiles and text blog data only. We approach the study of personality impressions in social media from the novel perspective of crowdsourced impressions, social attention, and audiovisual behavioral analysis on slices of conversational vlogs extracted from YouTube. Conversational vlogs are a unique case study to understand users in social media, as vloggers implicitly or explicitly share information about themselves that words, either written or spoken cannot convey. In addition, research in vlogs may become a fertile ground for the study of video interactions, as conversational video expands to innovative applications. In this work, we first investigate the feasibility of crowdsourcing personality impressions from vlogging as a way to obtain judgements from a variate audience that consumes social media video. Then, we explore how these personality impressions mediate the online video watching experience and relate to measures of attention in YouTube. Finally, we investigate on the use of automatic nonverbal cues as a suitable lens through which impressions are made, and we address the task of automatic prediction of vloggers' personality impressions using nonverbal cues and machine learning techniques. Our study, conducted on a dataset of 442 YouTube vlogs and 2210 annotations collected in Amazon's Mechanical Turk, provides new findings regarding the suitability of collecting personality impressions from crowdsourcing, the types of personality impressions that emerge through vlogging, their association with social attention, and the level of utilization of nonverbal cues in this particular setting. In addition, it constitutes a first attempt to address the task of automatic vlogger personality impression prediction using nonverbal cues, with promising results. Despite the evidence that social video conveys rich human personality information, research investigating the automatic prediction of personality impressions in vlogging has shown that, amongst the Big-Five traits, automatic nonverbal behavioral cues are useful to predict mainly the Extraversion trait. This finding, also reported in other conversational settings, indicates that personality information may be coded in other behavioral dimensions like the verbal channel, which has been less studied in multimodal interaction research. In this paper, we address the task of predicting personality impressions from vloggers based on what they say in their YouTube videos. First, we use manual transcripts of vlogs and verbal content analysis techniques to understand the ability of verbal content for the prediction of crowdsourced Big-Five personality impressions. Second, we explore the feasibility of a fully-automatic framework in which transcripts are obtained using automatic speech recognition (ASR). Our results show that the analysis of error-free verbal content is useful to predict four of the Big-Five traits, three of them better than using nonverbal cues, and that the errors caused by the ASR system decrease the performance significantly. In this paper we present the techniques used for the University of Montreal's team submissions to the 2013 Emotion Recognition in the Wild Challenge. The challenge is to classify the emotions expressed by the primary human subject in short video clips extracted from feature length movies. This involves the analysis of video clips of acted scenes lasting approximately one-two seconds, including the audio track which may contain human voices as well as background music. Our approach combines multiple deep neural networks for different data modalities, including: (1) a deep convolutional neural network for the analysis of facial expressions within video frames; (2) a deep belief net to capture audio information; (3) a deep autoencoder to model the spatio-temporal information produced by the human actions depicted within the entire scene; and (4) a shallow network architecture focused on extracted features of the mouth of the primary human subject in the scene. We discuss each of these techniques, their performance characteristics and different strategies to aggregate their predictions. Our best single model was a convolutional neural network trained to predict emotions from static frames using two large data sets, the Toronto Face Database and our own set of faces images harvested from Google image search, followed by a per frame aggregation strategy that used the challenge training data. This yielded a test set accuracy of 35.58 . Using our best strategy for aggregating our top performing models into a single predictor we were able to produce an accuracy of 41.03 on the challenge test set. These compare favorably to the challenge baseline test set accuracy of 27.56 . This paper addresses the problem of automatically inferring personality traits of people talking to a camera. As in many other computer vision problems, Convolutional Neural Networks (CNN) models have shown impressive results. However, despite of the success in terms of performance, it is unknown what internal representation emerges in the CNN. This paper presents a deep study on understanding why CNN models are performing surprisingly well in this complex problem. We use current techniques on CNN model interpretability, combined with face detection and Action Unit (AUs) recognition systems, to perform our quantitative studies. Our results show that: (1) face provides most of the discriminative information for personality trait inference, and (2) the internal CNN representations mainly analyze key face regions such as eyes, nose, and mouth. Finally, we study the contribution of AUs for personality trait inference, showing the influence of certain AUs in the facial trait judgments.