Effects of training on age estimation

The role of facial skin tone and texture in the perception of age

Age and gender perception from looking at people's faces, without any cultural or conventional cues, is primarily based on two independent components: a) the shape or facial structure, and b) surface reflectance (skin tone and texture, STT). This study examined the relative contribution of facial STT to the perception of age. A total of 204 subjects participated in four experiments presenting artificial 3D realistic faces of different age versions under two key experimental conditions: with and without STT. Two experiments involved a discrimination-age task, and other two involved a direct age-estimation task. The faces for the last experiment were generated from the photographs of real people. The results were quite consistent throughout the experiments. Data suggest that the contribution of the STT information leads to roughly 25-33 % of accuracy in age perception. Interestingly, a differential pattern emerges in relation to facial age: the relative contribution of skin information increases sharply with advancing age, to the point that age judgments of the older faces (60 years old) without STT information fall to the chance level. This pattern suggests that facial skin tone and texture are the main sources of information for estimating the age of people past their maturity as those are the principal visual signs of aging beyond the anatomical changes of facial structure.

Your ID , please? The effect of Facemasks and Makeup on Perceptions of Age of Young Adult Female Faces

During the COVID‐19 pandemic, wearing facemasks was mandatory in the United Kingdom except for individuals with medical exemptions. Facemasks cover the full lower half of the face; however, the effect of facemasks on age perception is not yet known. The present study examined whether age estimation accuracy of unfamiliar young adult women is impaired when the target is wearing a facemask. This study also examined whether makeup, which has previously been shown to increase error bias, further impairs age estimation accuracy when paired with a facemask. The findings indicate that both facemasks and makeup tend to result in overestimation of the young women's age compared to neutral faces, but the combination of both is not additive. Individual level analysis also revealed large individual differences in age estimation accuracy ranging from estimates within 1 year of the target's actual age, and age estimates which deviated by up to 20 years.

The AgeGuess database, an open online resource on chronological and perceived ages of people aged 5-100

In many developed countries, human life expectancy has doubled over the last 180 years. Underlying this higher life expectancy is a change in how we age. Biomarkers of ageing are used to quantify changes in the aging process and to determine biological age. Perceived age is such a biomarker that correlates with biological age. Here we present a unique database rich with possibilities to study the human ageing process. Using perceived age enables us to collect large amounts of data on biological age through a citizen science project, where people upload facial pictures and guess the ages of other people at www.ageguess.org . The data on perceived age we present here span birth cohorts from the years 1877 to 2012. The database currently contains around 220,000 perceived age guesses. Almost 4500 citizen scientists from over 120 countries of origin have uploaded ~4700 facial photographs. Beyond studying the ageing process, the data present a wealth of possibilities to study how humans guess ages and who is better at guessing ages.

Two sources of bias explain errors in facial age estimation

Accurate age estimates underpin our everyday social interactions, the provision of age-restricted services and police investigations. Previous work suggests that these judgements are error-prone, but the processes giving rise to these errors are not understood. Here, we present the first systematic test of bias in age estimation using a large database of standardized passport images of heterogeneous ages (n = 3948). In three experiments, we tested a range of perceiver age groups (n = 84), and found average age estimation error to be approximately 8 years. We show that this error can be attributed to two separable sources of bias. First, and accounting for the vast majority of variance, our results show an assimilative serial dependency whereby estimates are systematically biased towards the age of the preceding face. Second, younger faces are generally perceived to be older than they are, and older faces to be younger. In combination, these biases account for around 95% of variance in age estimates. We conclude that perception of age is modulated by representations that encode both a viewer's recent and normative exposure to faces. The finding that age perception is subject to strong top-down influences based on our immediate experience has implications for our understanding of perceptual processes involved in face perception, and for improving accuracy of age estimation in important real-world tasks.

So you think you look young? Matching older adults’ subjective ages with age estimations provided by younger, middle-aged, and older adults

Perceived age plays an important role in the context of age identity and social interactions. To examine how accurate individuals are in estimating how old they look and how old others are, younger, middle-aged, and older adults rated photographs of older target persons (for whom we had information about objective and subjective age) in terms of age and other characteristics. Whereas the older targets had quite accurate perceptions of how old they look, the raters’ age estimations for those targets were rather inaccurate. Older raters and those with frequent contact with older adults provided the most accurate age estimations. Targets that were rated more favorably with respect to attractiveness or fitness were perceived as younger than those with negative ratings.

Adaptation and the perception of facial age

We examined how the perceived age of adult faces is affected by adaptation to younger or older adult faces. Observers viewed images of a synthetic male face simulating ageing over a modelled range from 15 to 65 years. Age was varied by changing shape cues or textural cues. Age level was varied in a staircase to find the observer's subjective category boundary between "old" and "young". These boundaries were strongly biased by adaptation to the young or old face, with significant aftereffects induced by either shape or textural cues. A further experiment demonstrated comparable aftereffects for photorealistic images of average older or younger adult faces, and found that aftereffects showed some selectivity for a change in gender but also strongly transferred across gender. This transfer shows that adaptation can adjust to the attribute of age somewhat independently of other facial attributes. These findings suggest that perceived age, like many other natural facial dimensions, is highly susceptible to adaptation, and that this adaptation can be carried by both the structural and textural changes that normally accompany facial ageing.

Can undergraduate paramedic and nursing students accurately estimate patient age and weight?

INTRODUCTION

Accurate estimation of a patient's age and weight are skills expected of all healthcare clinicians, including paramedics and nurses. It is necessary because patients may be unable to communicate such information due to unconsciousness or an altered state of conscious. Age and weight estimation influence calculation for medication dosages, defibrillation, equipment sizing, and other invasive procedures such as intubation. The objective of this study was to identify whether undergraduate paramedic and nursing students were able to accurately estimate a patient's age and weight based on digital patient photos.

METHODS

A prospective, observational study involving undergraduate paramedic and nursing students from two Australian universities was used to estimate the age and weight of seven patients (adult and pediatric). Each patient image appeared in a PowerPoint presentation for 15 seconds, followed by a short pause, with the next patient image commencing automatically.

RESULTS

The findings demonstrated variable accuracy in age and weight estimation of the patients. Age estimations of pediatric patients were more accurate than estimations for adult patients. The majority of patient weights were under-estimated, with university undergraduate students in one university displaying similar estimations to the other university counterparts.

CONCLUSIONS

Results from this study identified variations in students' ability to accurately estimate a patient's age and weight. This study shows that consideration should be given to age and weight estimation education, which could be incorporated into undergraduate healthcare curriculum.

Experts on age estimation

The aim of this investigation was to study the biases and accuracy in age estimation of persons selling alcohol. Two experiments are reported, both suggesting that the accuracy in age estimation of Swedish alcohol salespersons is higher than that of control persons. This expertise in age estimation is probably the result of the extensive training Swedish alcohol salespersons go through as a natural part of their profession. Nonetheless, their estimates were not free from bias. Salespersons overestimated the age of target persons below 20 years of age and thus too young to buy alcohol. The results also revealed that controls, in contrast to salespersons, assimilated their estimates towards their own age (i.e. an own-anchor effect). Furthermore, female participants were shown to estimate the age of old target persons (56-65 years) more accurately than male participants. These results are discussed in relation to previous findings on training in age estimation and present jurisdiction.

Influence of facial skin attributes on the perceived age of Caucasian women

BACKGROUND AND OBJECTIVE

The facial appearance of a person does not always reflect the chronological age; some people look younger or older than they really are. Many studies have described the changes in skin properties (colour, wrinkles, sagging, micro relief, etc.) with age, but few of them have analysed their influence on the perceived age. The primary objective of this study was to assess the contribution of individual skin attributes of the face on the perceived age of Caucasian women. Secondary objectives were to assess the influence of age and gender of graders with regard to the age perception.

SUBJECTS AND METHOD

A random sample of 173 subjects of 20 to 74 years of age was taken from a database of more than 5000 healthy Caucasian women. A trained grader performed visual assessment of facial skin attributes (using a visual analogue scale), and a front face photograph was taken from each subject. Photographs were shown to 48 graders (20 men and 28 women, aged 22-64 years) who were asked to estimate the age of the subjects. Graders were classified as young (less than 35 years), middle age (35-50 years) and seniors (older than 50 years). Partial Least Square regression models were built to predict the chronological and the perceived age from the measured facial individual attributes. The contribution of each attribute within the regression model enabled to measure the relevance of this attribute with regards to age prediction.

RESULTS

The eye area and the skin colour uniformity were the main attributes related to perceived age. For age prediction, older graders' estimations were more driven by lips border definition shape and eyes opening, whereas younger graders' (older than 50 years) estimations were more driven by dark circles, nasolabial fold and brown spots. There were statistically significant differences in graders' age perception between gender and among age ranges. Our findings suggest that female graders are more accurate than male, and younger graders (under 35 years) are more accurate than older (over 50 years) to predict Caucasian women age from facial photographs.

CONCLUSIONS

Different skin attributes influence the estimation of age. These attributes have a different weight in the evaluation of the perceived age, depending on the age and of the observer. The most important attributes to estimate age are eyes, lips and skin colour uniformity.