Dynamics of human facial aging through photoanthropometric analysis and quadratic modeling from child- to adulthood – a multi-centric study

• Autor(a) principal: Lima, Laíse Nascimento Correia
• Data de Publicação: 2020
• Outros Autores: Santiago, Bianca Marques, Franco, Ademir, Thevissen, Patrick, Vidal, Flávio de Barros, Porto, Lucas Faria, Aguilera, Inmaculada Aleman, Machado, Carlos Eduardo Palhares, Daruge Jr, Eduardo
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Revista Brasileira de Ciências do Envelhecimento Humano (Online)
• Texto Completo: http://seer.upf.br/index.php/rbceh/article/view/9888

This study aimed to investigate the dynamics of facial aging from photographs of individuals in different age groups and establish a pattern of facial growth. The sample consisted of digital photographs standardly taken from 1273 Brazilians aged between 2 and 24 years and equally distributed by sex into 12 specific age groups (2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22 and 24 years). SAFF-2D® (Forensic Facial Analysis System, Brazilian Federal Police, Brazil) software package was used for positioning 28 landmarks on each photograph. Two-hundred and eight measurements were established between the landmarks. Photoanthropometric Indicators (PAI) of facial morphological alterations were obtained from the relations of facial measurements with a fixed reference (diameter of the iris). Non-transformed linear, quadratic and log-linear models were tested to screen the best approach to describe the facial growth with aging in females and males. The quadratic model reached the best outcomes in females (R2 <73.2%, mean: 52.14% ± 0.15) and males (R2 <80.8%, mean: 60.87% ± 0.15). Most of the PAI (>99.04%) were statistically associated with age in females and males (p<0.05). Vertical facial alterations were the most evident over the time, especially the height of the human face (p<0.05). Data extraction and treatment performed with the photoanthropometric analysis and the quadratic statistical model described the dynamics of facial growth by tackling facial allometry.