Concordant preferences for actual height and facial cues to height

Ré, Daniel; Perrett, David I.

doi:10.1016/j.paid.2012.07.001

Cited by 15 publications

(20 citation statements)

References 62 publications

(81 reference statements)

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“…It is worth noting that simply increasing the length of people's faces (in photographs) can lead observers to perceive them as taller (Re & Perrett, 2012). In the present studies, however, pictures were standardized to a height of 300 pixels after the frame was cropped as tightly as possible to the vertical extremes of the head.…”

Section: Discussionmentioning

confidence: 93%

Judgments of Height From Faces are Informed by Dominance and Facial Maturity

Burton¹,

Rule²

2013

Social Cognition

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Section: Discussionmentioning

confidence: 93%

Judgments of Height From Faces are Informed by Dominance and Facial Maturity

Burton¹,

Rule²

2013

Social Cognition

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“…Batres & Perrett, 2014;Quist, DeBruine, Little, & Jones, 2012;Re & Perrett, 2012). In all these experiments the faces were considered to show 'neutral' expressions.…”

Section: Discussionmentioning

confidence: 99%

“…This process (a) computes the average x and y values for 188 facial landmarks within the set of face images, (b) warps each shape of each facial image into these average coordinates, and then blends the warped component images (Re & Perrett, 2012;Tiddeman et al, 2001). These average images were then made symmetrical to emphasize the role of the cues being investigated (see Perrett et al, 1999).…”

Section: Facial Averagingmentioning

confidence: 99%

Eyelid-openness and mouth curvature influence perceived intelligence beyond attractiveness.

Talamas¹,

Mavor²,

Axelsson³

et al. 2016

Journal of Experimental Psychology: General

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Impression formation is profoundly influenced by facial attractiveness, but the existence of facial cues which affect judgments beyond such an “attractiveness halo” may be underestimated. Because depression and tiredness adversely affect cognitive capacity, we reasoned that facial cues to mood (mouth curvature) and alertness (eyelid-openness) affect impressions of intellectual capacity. Over 4 studies we investigated the influence of these malleable facial cues on first impressions of intelligence. In Studies 1 and 2 we scrutinize the perceived intelligence and attractiveness ratings of images of 100 adults (aged 18–33) and 90 school-age children (aged 5–17), respectively. Intelligence impression was partially mediated by attractiveness, but independent effects of eyelid-openness and subtle smiling were found that enhanced intelligence ratings independent of attractiveness. In Study 3 we digitally manipulated stimuli to have altered eyelid-openness or mouth curvature and found that each independent manipulation had an influence on perceptions of intelligence. In a final set of stimuli (Study 4) we explored changes in these cues before and after sleep restriction, to examine whether natural variations in these cues according to sleep condition can influence perceptions. In Studies 3 and 4 variations with increased eyelid-openness and mouth curvature were found to relate positively to intelligence ratings. These findings suggest potential overgeneralizations based on subtle facial cues that indicate mood and tiredness, both of which alter cognitive ability. These findings also have important implications for students who are directly influenced by expectations of ability and teachers who may form expectations based on initial perceptions of intelligence.

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“…St. Andrews Professor Perritt [5] sought to answer the question first in in 1994 in a study related to men's perception of women's' faces having various emotion. He discovered that men from different cultures perceived the same group of women as beautiful and tried to learn the highest ranking (most beautiful) features through CV techniques.…”

Section: Knowledge Lda Data Mining -Past and Current State Of Affairsmentioning

confidence: 99%

“…Thus, the 'above average' (see figure2) was added to original imagery, making it very difficult (due to blur/artifacts) for computer vision to learn what defines beauty. [3], (b) average face beauty add to normal faces increased their ranking [4], (c) Apparent height of the face has positive correlation in men's perception of beauty, opposite in women [5].…”

Section: Knowledge Lda Data Mining -Past and Current State Of Affairsmentioning

confidence: 99%

e-IQ and IQ knowledge mining for generalized LDA

Jenkins

Bergem

Sweet³

et al. 2015

SPIE Proceedings

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How can the human brain uncover patterns, associations and features in real-time, real-world data? There must be a general strategy used to transform raw signals into useful features, but representing this generalization in the context of our information extraction tool set is lacking. In contrast to Big Data (BD), Large Data Analysis (LDA) has become a reachable multi-disciplinary goal in recent years due in part to high performance computers and algorithm development, as well as the availability of large data sets. However, the experience of Machine Learning (ML) and information communities has not been generalized into an intuitive framework that is useful to researchers across disciplines. The data exploration phase of data mining is a prime example of this unspoken, ad-hoc nature of ML -the Computer Scientist works with a Subject Matter Expert (SME) to understand the data, and then build tools (i.e. classifiers, etc.) which can benefit the SME and the rest of the researchers in that field. We ask, why is there not a tool to represent information in a meaningful way to the researcher asking the question? Meaning is subjective and contextual across disciplines, so to ensure robustness, we draw examples from several disciplines and propose a generalized LDA framework for independent data understanding of heterogeneous sources which contribute to Knowledge Discovery in Databases (KDD). Then, we explore the concept of adaptive Information resolution through a 6W unsupervised learning methodology feedback system. In this paper, we will describe the general process of man-machine interaction in terms of an asymmetric directed graph theory (digging for embedded knowledge), and model the inverse machine-man feedback (digging for tacit knowledge) as an ANN unsupervised learning methodology. Finally, we propose a collective learning framework which utilizes a 6W semantic topology to organize heterogeneous knowledge and diffuse information to entities within a society in a personalized way.

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Concordant preferences for actual height and facial cues to height

Cited by 15 publications

References 62 publications

Judgments of Height From Faces are Informed by Dominance and Facial Maturity

Judgments of Height From Faces are Informed by Dominance and Facial Maturity

Eyelid-openness and mouth curvature influence perceived intelligence beyond attractiveness.

e-IQ and IQ knowledge mining for generalized LDA

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