Scoring facial attractiveness with deep convolutional neural networks: How training on standardized images reduces the bias of facial expressions.

Objective: In many medical disciplines, facial attractiveness is part of the diagnosis, yet its scoring might be confounded by facial expressions. The intent was to apply deep convolutional neural networks (CNN) to identify how facial expressions affect facial attractiveness and to explore whether a dedicated training of the CNN is able to reduce the bias of facial expressions.

Materials And Methods: Frontal facial images (n = 840) of 40 female participants (mean age 24.

Using artificial intelligence to determine the influence of dental aesthetics on facial attractiveness in comparison to other facial modifications.

Background: Facial aesthetics is a major motivating factor for undergoing orthodontic treatment.

Objectives: To ascertain-by means of artificial intelligence (AI)-the influence of dental alignment on facial attractiveness and perceived age, compared to other modifications such as wearing glasses, earrings, or lipstick.

Material And Methods: Forty volunteering females (mean age: 24.