Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition

Here, we develop an audiovisual deep residual network for multimodal apparent personality trait recognition. The network is trained end-to-end for predicting the Big Five personality traits of people from their videos. That is, the network does not require any feature engineering or visual analysis such as face detection, face landmark alignment or facial expression recognition. Recently, the network won the third place in the ChaLearn First Impressions Challenge with a test accuracy of 0.9109

How do you say ‘hello’? Personality impressions from brief novel voices

On hearing a novel voice, listeners readily form personality impressions of that speaker. Accurate or not, these impressions are known to affect subsequent interactions; yet the underlying psychological and acoustical bases remain poorly understood. Furthermore, hitherto studies have focussed on extended speech as opposed to analysing the instantaneous impressions we obtain from first experience. In this paper, through a mass online rating experiment, 320 participants rated 64 sub-second vocal utterances of the word ‘hello’ on one of 10 personality traits. We show that: (1) personality judgements of brief utterances from unfamiliar speakers are consistent across listeners; (2) a two-dimensional ‘social voice space’ with axes mapping Valence (Trust, Likeability) and Dominance, each driven by differing combinations of vocal acoustics, adequately summarises ratings in both male and female voices; and (3) a positive combination of Valence and Dominance results in increased perceived male vocal Attractiveness, whereas perceived female vocal Attractiveness is largely controlled by increasing Valence. Results are discussed in relation to the rapid evaluation of personality and, in turn, the intent of others, as being driven by survival mechanisms via approach or avoidance behaviours. These findings provide empirical bases for predicting personality impressions from acoustical analyses of short utterances and for generating desired personality impressions in artificial voices

Unfakeable facial configurations affect strategic choices in trust games with or without information about past behavior

Background Many human interactions are built on trust, so widespread confidence in first impressions generally favors individuals with trustworthy-looking appearances. However, few studies have explicitly examined: 1) the contribution of unfakeable facial features to trust-based decisions, and 2) how these cues are integrated with information about past behavior. Methodology/Principal Findings Using highly controlled stimuli and an improved experimental procedure, we show that unfakeable facial features associated with the appearance of trustworthiness attract higher investments in trust games. The facial trustworthiness premium is large for decisions based solely on faces, with trustworthy identities attracting 42% more money (Study 1), and remains significant though reduced to 6% when reputational information is also available (Study 2). The face trustworthiness premium persists with real (rather than virtual) currency and when higher payoffs are at stake (Study 3). Conclusions/Significance Our results demonstrate that cooperation may be affected not only by controllable appearance cues (e.g., clothing, facial expressions) as shown previously, but also by features that are impossible to mimic (e.g., individual facial structure). This unfakeable face trustworthiness effect is not limited to the rare situations where people lack any information about their partners, but survives in richer environments where relevant details about partner past behavior are available