Automatic classification of human facial features based on their appearance

Facial morphological analysis for the development of a representative atlas of the brazilian population

The objective of this study was to propose categories of morphological classification for the face and its anatomical structures, as well as to propose illustrations to support the development of an atlas that facilitates facial morphological analysis of adult Brazilians. It was a descriptive study based on the analysis of the frequency and distribution of 13 photoanthropometric facial ratios (RFAs) obtained from a representative sample of the Brazilian population. RFAs related to facial height and width, eye width, intercanthal distance, nose length and width, philtrum ridge height and width, mouth thickness and width, upper and lower lip thickness, and chin height were analyzed. The study included a sample of 5.000 individuals aged between 18 and 22 years, evenly distributed between genders. Data normality was assessed using the Shapiro-Wilk test, considering them as parametric when p > 0.05. For the RFAs that showed normal distribution, mean ± 1.5 standard deviations (SD) were used to categorize facial measurements as regular, below average, or above average. Non-parametric RFAs were analyzed based on the median and 10th and 90th percentiles of the data. Based on the established average iris diameter, which is considered the most stable facial measurement, the values of the described RFAs were converted to a numerical scale in centimeters, allowing for the illustration of female and male faces. In this way, it was possible to categorize the facial anatomical structures and, consequently, visualize the facial morphological pattern of the adult Brazilian population.

The Development and Adoption of Online Learning in Pre- and Post-COVID-19: Combination of Technological System Evolution Theory and Unified Theory of Acceptance and Use of Technology

After the outbreak of COVID-19, schools heavily depend on e-learning technologies and tools to shift from in-person class to online. This review article analyzes the changes of technology evolution and technology adoption of e-learning in pre- and post-COVID-19 based on the Technology System Evaluation Theory (TSET) and technology adoption of e-learning based on the Unified Theory of Acceptance and Use of Technology (UTAUT). We intend to explore the interaction of technology evolution and technology adoption in the different focus of e-learning technology in the two stages and the particularity and heterogeneity of the UTAUT model. The results indicate that (1) The moderating results of technology evolution are proposed and evaluated under the UTAUT model before the COVID-19 outbreak. Studies after the COVID-19 pandemic paid more attention to technology efficiency rather than effectiveness; (2) Research on e-learning focuses on the infrastructure to reach more users after the outbreak of COVID-19 because e-learning is the only way to continue education; (3) COVID-19 fear moderates the relationship between the external factors and the behavior intention of e-learning users. The lack of financial support on technology evolution will directly weaken the implementation of new technology. Social Isolation offers more opportunities for students to engage in e-learning. Meanwhile, it slows down the implementation of e-learning because of out-to-date hardware and software. This article offers an enhanced understanding of the interaction of technology evolution and technology adoption under unexpected environments and provides practical insights into how to promote new technology in a way that users will accept and use easily. This study can be tested and extended by empirical research in the future.

The Influence of Each Facial Feature on How We Perceive and Interpret Human Faces

Facial information is processed by our brain in such a way that we immediately make judgments about, for example, attractiveness or masculinity or interpret personality traits or moods of other people. The appearance of each facial feature has an effect on our perception of facial traits. This research addresses the problem of measuring the size of these effects for five facial features (eyes, eyebrows, nose, mouth, and jaw). Our proposal is a mixed feature-based and image-based approach that allows judgments to be made on complete real faces in the categorization tasks, more than on synthetic, noisy, or partial faces that can influence the assessment. Each facial feature of the faces is automatically classified considering their global appearance using principal component analysis. Using this procedure, we establish a reduced set of relevant specific attributes (each one describing a complete facial feature) to characterize faces. In this way, a more direct link can be established between perceived facial traits and what people intuitively consider an eye, an eyebrow, a nose, a mouth, or a jaw. A set of 92 male faces were classified using this procedure, and the results were related to their scores in 15 perceived facial traits. We show that the relevant features greatly depend on what we are trying to judge. Globally, the eyes have the greatest effect. However, other facial features are more relevant for some judgments like the mouth for happiness and femininity or the nose for dominance.

A data-driven classification of 3D foot types by archetypal shapes based on landmarks

The taxonomy of foot shapes or other parts of the body is important, especially for design purposes. We propose a methodology based on archetypoid analysis (ADA) that overcomes the weaknesses of previous methodologies used to establish typologies. ADA is an objective, data-driven methodology that seeks extreme patterns, the archetypal profiles in the data. ADA also explains the data as percentages of the archetypal patterns, which makes this technique understandable and accessible even for non-experts. Clustering techniques are usually considered for establishing taxonomies, but we will show that finding the purest or most extreme patterns is more appropriate than using the central points returned by clustering techniques. We apply the methodology to an anthropometric database of 775 3D right foot scans representing the Spanish adult female and male population for footwear design. Each foot is described by a 5626 × 3 configuration matrix of landmarks. No multivariate features are used for establishing the taxonomy, but all the information gathered from the 3D scanning is employed. We use ADA for shapes described by landmarks. Women’s and men’s feet are analyzed separately. We have analyzed 3 archetypal feet for both men and women. These archetypal feet could not have been recovered using multivariate techniques.