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Comparative analysis of original face and skin-warped average face images for the scoring of skin attributes. | LitMetric

AI Article Synopsis

  • The study aimed to analyze skin changes using an average face image to avoid issues with image rights, involving landmark-based warping of individual skin images onto this average face.
  • An average front face image was created from 71 Japanese women aged 50-60, and experts evaluated various skin features from both original and warped images, finding strong correlations in scoring.
  • Results showed that grading of facial characteristics, including perceived age, was highly consistent between the original and warped images, suggesting this method could effectively track skin changes and respect image rights in future studies.

Article Abstract

Objectives: Representative of a panel, an average face image could be used to analyse/display skin changes while alleviating image rights constraints. Therefore, we used landmark-based deformation (warping) of individual skin images onto their panel's average face, evaluating this approach's relevance and possible limits.

Methods: An average front face image was constructed from images of 71 Japanese women (50-60 years old). After warping individual skin images onto this average face, the resulting skin-warped average faces were presented to three experts who graded: forehead wrinkles, nasolabial fold, wrinkle of the corner of the lips, pore visibility and skin pigmentation homogeneity. Two experts estimated subjects' age. Results were compared to gradings performed on original images.

Results: Inter-expert grading shows excellent to good correlation whatever image type: from 0.918 (forehead wrinkles) to 0.693 (visibility of pores). Correlations between scoring of both image types are almost always higher than inter-expert correlations (maximum: 0.939 for forehead wrinkles-minimum: 0.677 for pore visibility). Frequencies of grades/ages are similar when scoring original and skin-warped average face images. Experts scores are similar in 90.6%-99.3% of the cases. Average deviations upon scoring both image types are smaller than average inter-expert deviations on original images.

Conclusions: Scoring facial characteristics in original images and skin-warped average face images show an excellent agreement, even for perceived age, a complex feature. This opens the possibility of using this approach to grade facial skin features, monitor changes over time, and to valorise results on a face deprived of image rights.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10234171PMC
http://dx.doi.org/10.1111/srt.13324DOI Listing

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