Facial cues to age perception using three-dimensional analysis

Coordinate-wise monotonic transformations enable privacy-preserving age estimation with 3D face point cloud

The human face is a valuable biomarker of aging, but the collection and use of its image raise significant privacy concerns. Here we present an approach for facial data masking that preserves age-related features using coordinate-wise monotonic transformations. We first develop a deep learning model that estimates age directly from non-registered face point clouds with high accuracy and generalizability. We show that the model learns a highly indistinguishable mapping using faces treated with coordinate-wise monotonic transformations, indicating that the relative positioning of facial information is a low-level biomarker of facial aging. Through visual perception tests and computational 3D face verification experiments, we demonstrate that transformed faces are significantly more difficult to perceive for human but not for machines, except when only the face shape information is accessible. Our study leads to a facial data protection guideline that has the potential to broaden public access to face datasets with minimized privacy risks.

Clues for Facial Perceived Age: Exercise, Sun Protection, Photoaging, and Anthropometric Properties: An Observational Cross-Sectional Study

BACKGROUND AND OBJECTIVES

The effect of environmental and genetic factors on the aging process is widely acknowledged. Yet, the extent to which each factor decisively contributes to the perception of looking younger or older remains a subject of debate. This study seeks to identify the factors linked to the perceived age among Turkish women.

PATIENTS AND METHODS

Ten assessors scored the perceived ages of 250 female patients based on facial photographs. The study aimed to assess the impact of environmental factors and anthropometric measurements on the perception of aging. A comprehensive analysis involved conducting 9 perioral and 6 periorbital anthropometric measurements on all study participants.

RESULTS

Exercise (p = .001), mild photodamage (stage 1-2) (p = .001), consistent sunscreen use (p = .001), the length of the palpebral fissure (p = .043), and the height of the upper vermilion (p = .019) demonstrated significant associations with a more youthful appearance.

CONCLUSION

Environmental factors, including exercise, photoprotection, sunscreen use, and anthropometric measurements such as palpebral fissure length and upper vermilion height, play a significant role in contributing to a more youthful appearance.

Accurate estimation of biological age and its application in disease prediction using a multimodal image Transformer system

Aging in an individual refers to the temporal change, mostly decline, in the body's ability to meet physiological demands. Biological age (BA) is a biomarker of chronological aging and can be used to stratify populations to predict certain age-related chronic diseases. BA can be predicted from biomedical features such as brain MRI, retinal, or facial images, but the inherent heterogeneity in the aging process limits the usefulness of BA predicted from individual body systems. In this paper, we developed a multimodal Transformer-based architecture with cross-attention which was able to combine facial, tongue, and retinal images to estimate BA. We trained our model using facial, tongue, and retinal images from 11,223 healthy subjects and demonstrated that using a fusion of the three image modalities achieved the most accurate BA predictions. We validated our approach on a test population of 2,840 individuals with six chronic diseases and obtained significant difference between chronological age and BA (AgeDiff) than that of healthy subjects. We showed that AgeDiff has the potential to be utilized as a standalone biomarker or conjunctively alongside other known factors for risk stratification and progression prediction of chronic diseases. Our results therefore highlight the feasibility of using multimodal images to estimate and interrogate the aging process.

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.

DeepSmile: Anomaly Detection Software for Facial Movement Assessment

Facial movements are crucial for human interaction because they provide relevant information on verbal and non-verbal communication and social interactions. From a clinical point of view, the analysis of facial movements is important for diagnosis, follow-up, drug therapy, and surgical treatment. Current methods of assessing facial palsy are either (i) objective but inaccurate, (ii) subjective and, thus, depending on the clinician's level of experience, or (iii) based on static data. To address the aforementioned problems, we implemented a deep learning algorithm to assess facial movements during smiling. Such a model was trained on a dataset that contains healthy smiles only following an anomaly detection strategy. Generally speaking, the degree of anomaly is computed by comparing the model's suggested healthy smile with the person's actual smile. The experimentation showed that the model successfully computed a high degree of anomaly when assessing the patients' smiles. Furthermore, a graphical user interface was developed to test its practical usage in a clinical routine. In conclusion, we present a deep learning model, implemented on open-source software, designed to help clinicians to assess facial movements.

The Esthetics of the Nasolabial Fold and Age in the Elderly Via Eye-Tracking

Background

Facial aging is associated with the loss of soft tissue fullness. Perioral signs of facial aging can add years to individuals' appearances and even affect their facial expressions in smiling and nonsmiling images.

Aims

To evaluate the influence of the nasolabial fold on the visual perception of esthetics and age in the elderly, eye-tracking and a visual analog scale were used.

Material and Methods

This study applied a cross-sectional study using 40 laypeople. Facial images of an elderly woman were modified to include facial expression lines such as nasolabial folds and marionette lines with no folds, intermediate, and accentuated depths folds. Eye tracking was implemented to measure the average number of fixations. Heat maps and dot maps were generated using eye-tracking software. A visual analog scale of attractiveness and age perception questionnaire were also incorporated into the study. Statistical analysis was performed using a significance of (P<0.05).

Results

The majority of visual attention was paid to the eye and mouth areas. In the images with no folds, the right eye attracted a greater degree of fixation. In the images in which nasolabial folds were accentuated, the mouth area served as an area of high fixation. No statistical difference was observed between the groups in which smiling images were viewed. Nonsmiling images demonstrated significant differences between groups for variables including time until first fixation, complete fixation time, and number of fixations on the eyes and hemifaces. Old age and diminished attractiveness were associated with attenuated nasolabial folds, especially in nonsmiling images.

Conclusion

The facial lines and expressions of elderly female individuals were assessed by laypeople using eye-tracking, showing that the deeper one's nasolabial folds, the more aged and less attractive one appears, especially in nonsmiling images. Smiling pictures were perceived to be more attractive and youthful; these perceptions should be considered in the search for improved esthetic results, whether in dental or facial esthetic treatments.

Differences between perceived age and chronological age in women: A multi-ethnic and multi-centre study

Objective

Accuracy in assessing age from facial cues is important in social perception given reports of strong negative correlations between perceived age and assessments of health and attractiveness. In a multi‐ethnic and multi‐centre study, we previously documented similar patterns of female facial age assessments across ethnicities, influenced by gender and ethnicity of assessors.

Methods

Here we extend these findings by examining differences between estimated age from digital portraits and chronological age (Δ age) for 180 women from three age groups (20–34, 35–49, 50–66 years) and five ethnicities (36 images of each ethnicity, assessed for age on a continuous scale by 120 female and male raters of each ethnicity).

Results

Across ethnicities, Δ age was smallest in French assessors and largest in South African assessors. Numerically, French women were judged oldest and Chinese women youngest relative to chronological age. In younger women, Δ age was larger than in middle‐aged and older women. This effect was particularly evident when considering the interaction of women's age with assessor gender and ethnicity, independently and together, on Δ age.

Conclusion

Collectively, our findings suggest that accuracy in assessments of female age from digital portraits depends on the chronological age and ethnicity of the photographed women and the ethnicity and gender of the assessor. We discuss the findings concerning ethnic variation in skin pigmentation and visible signs of ageing and comment on implications for cosmetic science.

How Old Do I Look? Exploring the Facial Cues of Age in a Tasked Eye-Tracking Study

Importance: This is the first eye-tracking study to use a tasked age estimation paradigm to explore the facial cues of age as seen by casual observers. Objectives: Determine where observers gaze on faces when tasked with estimating an individual's age. Design, Setting, and Participants: This was a prospective controlled experiment, which took place at an academic tertiary referral center. In total, 220 casual observers (80 untasked, 140 tasked) viewed frontal facial images of women while an infrared eye-tracking monitor recorded their eye movements and fixations in real time. Main Outcomes and Measures: Multivariate Hotelling's analysis followed by planned posthypothesis testing was used to compare fixation durations for predefined regions of interest, including the central triangle, upper face, midface, lower face, and neck between tasked and untasked observers. Results: A total of 80 observers (mean age 23.6 years, 53% female) successfully completed the first untasked eye-tracking experiment. A total of 140 observers (mean age 26.1 years, 60% female) successfully completed the second age estimation experiment. On multivariate analysis, there were significant differences in the distribution of attention between observers in the two experiments (T² = 99.70; F(5,2084) = 19.9012, p < 0.0001). On planned posthypothesis testing, observers attended significantly more to the lower third of the face (0.20 s, p < 0.0001, 95% confidence interval (CI) 0.14-0.27 s) and neck (0.05 s, p = 0.0074, 95% CI 0.01-0.08 s) and less to the upper third of the face (-0.27 s, p < 0.0001, 95% CI -0.40 to -0.14 s) when tasked. There was no significant difference in time spent on the whole face in the two experiments, suggesting that peripheral elements such as hair color or jewelry did not significantly influence gaze patterns. Conclusions and Relevance: Humans form judgments about others every day of their lives, and age perception colors their every interaction. To our knowledge, this study is the first to use eye tracking to investigate facial cues of age. The results showed that when tasked with estimating age, casual observer visual attention was shifted toward the lower face when compared with those who were untasked. These data inform our understanding of facial age perception and potential areas to target for facial rejuvenation. Level of Evidence: NA.