Exploring Relationship Between Face and Trustworthy Impression Using Mid-level Facial Features

Yan, Yan; Nie, Jie; Huang, Lei; Li, Zhen; Cao, Qinglei; Wei, Zhiqiang

doi:10.1007/978-3-319-27671-7_45

Cited by 6 publications

(6 citation statements)

References 15 publications

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“…Mid-level features. Facial landmarks seem to be the start point of different feature extraction methods [48], especially those which exploit mid-level features or semantic attributes [46], [47]. Mid-level features or semantic attributes usually carry meaningful information, which can be used to complement other low-level features and then improve accuracy performance.…”

Section: Discussionmentioning

confidence: 99%

“…Current limitations. Most works presented in this section either built their own datasets [46] (e.g., collecting data from the Internet) or adapted to their needs [47], [49] datasets developed for other purposes (e.g., face recognition). The common point of these works is that images need to be labelled, and labels usually do not become public.…”

Section: Discussionmentioning

confidence: 99%

“…Even though a small dataset is used (i.e., composed of 123 images from different users), authors presented some insights on which mid-level cues contribute to personality recognition and perception (see Table 2). Yan et al [47] studied the relationship between facial appearance and personality impressions in the form of trustworthy. In their work, different low-level features are extracted from different face regions, as well as relationships between regions.…”

Section: Still Imagesmentioning

confidence: 99%

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First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysis

Jacques

Güçlütürk

Pérez

et al. 2022

IEEE Trans. Affective Comput.

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Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Still Imagesmentioning

confidence: 99%

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First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysis

Jacques

Güçlütürk

Pérez

et al. 2022

IEEE Trans. Affective Comput.

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“…With the advances in deep learning, use of computational methods to assess apparent personality traits has started to attract more interest from computer scientists. Recent studies mainly utilize three different modalities: vision-based, focusing on images [37,66,89], and focusing on videos [6,10,17], audio-based [77], and language-based [4]. Visionbased methods are generally utilized more commonly than any other modalities [72].…”

Section: Analysis Of Apparent Personality Traitsmentioning

confidence: 99%

Multimodal analysis of personality traits on videos of self-presentation and induced behavior

Giritlioğlu

Mandira

Yılmaz

et al. 2020

J Multimodal User Interfaces

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Personality analysis is an important area of research in several fields, including psychology, psychiatry, and neuroscience. With the recent dramatic improvements in machine learning, it has also become a popular research area in computer science. While the current computational methods are able to interpret behavioral cues (e.g., facial expressions, gesture, and voice) to estimate the level of (apparent) personality traits, accessible assessment tools are still substandard for practical use, not to mention the need for fast and accurate methods for such analyses. In this study, we present multimodal deep architectures to estimate the Big Five personality traits from (temporal) audio-visual cues and transcribed speech. Furthermore, for a detailed analysis of personality traits, we have collected a new audio-visual dataset, namely: Self-presentation and Induced Behavior Archive for Personality Analysis (SIAP). In contrast to the available datasets, SIAP introduces recordings of induced behavior in addition to self-presentation (speech) videos. With thorough experiments on SIAP and ChaLearn LAP First Impressions datasets, we systematically assess the reliability of different behavioral modalities and their combined use. Furthermore, we investigate the characteristics and discriminative power of induced behavior for personality analysis, showing that the induced behavior indeed includes signs of personality traits.

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“…One of the main goals of the studies of computer visionbased apparent personality trait recognition and analysis is to increase our understanding of the underlying psychological phenomena through modeling. Previous work found in the literature proposed to explore the relationship between image features and first impressions [20,18] in order to determine which region is more relevant to predict different personality traits. However, facial features (e.g., region based or action units analysis) or facial expressions [15] have been more in the focus of such analysis, and the effects of other features, such as the background region and/or audio have not been as adequately studied.…”

Section: Introductionmentioning

confidence: 99%

Visualizing Apparent Personality Analysis with Deep Residual Networks

Güçlütürk

Güçlü

Pérez

et al. 2017

2017 IEEE International Conference on Computer Vision Workshops (ICCVW)

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Automatic prediction of personality traits is a subjective task that has recently received much attention. Specifically, automatic apparent personality trait prediction from multimodal data has emerged as a hot topic within the filed of computer vision and, more particularly, the so called "looking at people" sub-field. Considering "apparent" personality traits as opposed to real ones considerably reduces the subjectivity of the task. The real world applications are encountered in a wide range of domains, including entertainment, health, human computer interaction, recruitment and security. Predictive models of personality traits are useful for individuals in many scenarios (e.g., preparing for job interviews, preparing for public speaking). However, these predictions in and of themselves might be deemed to be untrustworthy without human understandable supportive evidence. Through a series of experiments on a recently released benchmark dataset for automatic apparent personality trait prediction, this paper characterizes the audio and visual information that is used by a state-of-the-art model while making its predictions, so as to provide such supportive evidence by explaining predictions made. Additionally, the paper describes a new web application, which gives feedback on apparent personality traits of its users by combining model predictions with their explanations.

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Exploring Relationship Between Face and Trustworthy Impression Using Mid-level Facial Features

Cited by 6 publications

References 15 publications

First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysis

First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysis

Multimodal analysis of personality traits on videos of self-presentation and induced behavior

Visualizing Apparent Personality Analysis with Deep Residual Networks

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