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Diagnostic Features for Human Categorisation of Adult and Child Faces.

ABSTRACT: Knowing how humans differentiate children from adults has useful implications in many areas of both forensic and cognitive psychology. Yet, how we extract age from faces has been surprisingly underexplored in both disciplines. Here, we used a novel data-driven experimental technique to objectively measure the facial features human observers use to categorise child and adult faces. Relying on more than 35,000 trials, we used a reverse correlation technique that enabled us to reveal how specific features which are known to be important in face-perception - position, spatial-frequency (SF), and orientation - are associated with accurate child and adult discrimination. This showed that human observers relied on evidence in the nasal bone and eyebrow area for accurate adult categorisation, while they relied on the eye and jawline area to accurately categorise child faces. For orientation structure, only facial information of vertical orientation was linked to face-adult categorisation, while features of horizontal and, to a lesser extent oblique orientations, were more diagnostic of a child face. Finally, we found that SF diagnosticity showed a U-shaped pattern for face-age categorisation, with information in low and high SFs being diagnostic of child faces, and mid SFs being diagnostic of adult faces. Through this first characterisation of the facial features of face-age categorisation, we show that important information found in psychophysical studies of face-perception in general (i.e., the eye area, horizontals, and mid-level SFs) is crucial to the practical context of face-age categorisation, and present data-driven procedures through which face-age classification training could be implemented for real-world challenges.

SUBMITTER: Faghel-Soubeyrand S

PROVIDER: S-EPMC8640236 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:In this paper we review the associations between maternal and child undernutrition with human capital and risk of adult diseases in low-income and middle-income countries. We analysed data from five long-standing prospective cohort studies from Brazil, Guatemala, India, the Philippines, and South Africa and noted that indices of maternal and child undernutrition (maternal height, birthweight, intrauterine growth restriction, and weight, height, and body-mass index at 2 years according to the new WHO growth standards) were related to adult outcomes (height, schooling, income or assets, offspring birthweight, body-mass index, glucose concentrations, blood pressure). We undertook systematic reviews of studies from low-income and middle-income countries for these outcomes and for indicators related to blood lipids, cardiovascular disease, lung and immune function, cancers, osteoporosis, and mental illness. Undernutrition was strongly associated, both in the review of published work and in new analyses, with shorter adult height, less schooling, reduced economic productivity, and--for women--lower offspring birthweight. Associations with adult disease indicators were not so clear-cut. Increased size at birth and in childhood were positively associated with adult body-mass index and to a lesser extent with blood pressure values, but not with blood glucose concentrations. In our new analyses and in published work, lower birthweight and undernutrition in childhood were risk factors for high glucose concentrations, blood pressure, and harmful lipid profiles once adult body-mass index and height were adjusted for, suggesting that rapid postnatal weight gain--especially after infancy--is linked to these conditions. The review of published works indicates that there is insufficient information about long-term changes in immune function, blood lipids, or osteoporosis indicators. Birthweight is positively associated with lung function and with the incidence of some cancers, and undernutrition could be associated with mental illness. We noted that height-for-age at 2 years was the best predictor of human capital and that undernutrition is associated with lower human capital. We conclude that damage suffered in early life leads to permanent impairment, and might also affect future generations. Its prevention will probably bring about important health, educational, and economic benefits. Chronic diseases are especially common in undernourished children who experience rapid weight gain after infancy.

Project description:The appearance-based approach to face detection has seen great advances in the last several years. In this approach, we learn the image statistics describing the texture pattern (appearance) of the object class we want to detect, e.g., the face. However, this approach has had limited success in providing an accurate and detailed description of the internal facial features, i.e., eyes, brows, nose, and mouth. In general, this is due to the limited information carried by the learned statistical model. While the face template is relatively rich in texture, facial features (e.g., eyes, nose, and mouth) do not carry enough discriminative information to tell them apart from all possible background images. We resolve this problem by adding the context information of each facial feature in the design of the statistical model. In the proposed approach, the context information defines the image statistics most correlated with the surroundings of each facial component. This means that when we search for a face or facial feature, we look for those locations which most resemble the feature yet are most dissimilar to its context. This dissimilarity with the context features forces the detector to gravitate toward an accurate estimate of the position of the facial feature. Learning to discriminate between feature and context templates is difficult, however, because the context and the texture of the facial features vary widely under changing expression, pose, and illumination, and may even resemble one another. We address this problem with the use of subclass divisions. We derive two algorithms to automatically divide the training samples of each facial feature into a set of subclasses, each representing a distinct construction of the same facial component (e.g., closed versus open eyes) or its context (e.g., different hairstyles). The first algorithm is based on a discriminant analysis formulation. The second algorithm is an extension of the AdaBoost approach. We provide extensive experimental results using still images and video sequences for a total of 3,930 images. We show that the results are almost as good as those obtained with manual detection.

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Diagnostic Features for Human Categorisation of Adult and Child Faces.

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