102 ANTI-AGEING A 0 -5
-10 -15
-20 -25 -30
Placebo B 0 days **** -19.9% A 0
-2 -4 -6 -8
-10 -12 -14 -16
Placebo
-10.9% 2% new
active ingredient 2% new active ingredient
Figure 3: A. Variation (%) in skin wrinkle coefficient of visibility obtained after 28 days of product application (*p<0.05 versus initial time) versus placebo. Student’s t test was used after checking the normality distributions by a Shapiro-Wilk test. B. CameraScan images of a volunteer before and after 28 days of treatment with the active ingredient
Minimized appearance of expression lines in the crow’s feet area Lateral macrophotographs of the face were taken with the software CameraScan (Orion Concept) before and after 28 days of product use. Variations in the coefficient of visibility of wrinkles on the crow’s feet area were determined with the FrameScan software (Orion Concept). As shown in Figure 3A, after 28 days of
28 days
treatment, the coefficient of visibility of wrinkles significantly decreased by -10.9% on volunteers that applied the foundation containing the new active ingredient. Images of the volunteer’s crow’s feet area at both times of the study (Figure 3A) show the improvement in the appearance of expression lines over time.
Figure 2: A. Variation (%) in skin wrinkle area obtained after 28 days of product application (****p<0.0001 versus initial time) versus placebo with a smile expression. Student’s t test was used after checking the normality distributions by a Shapiro-Wilk test. B. Macroscopic images of a volunteer smiling before and after 28 days of treatment with the active ingredient
Neutral
Smile rating technology Artificial intelligence (AI) is being used more and more for research and innovation in the beauty industry. Algorithms can learn from data and help us explore new ways of analysis. Lubrizol has developed the smile rating technology, a deep learning-based framework that is able to objectively recognize and quantify the emotions of volunteers. As shown in Figure 4, this technology
classifies facial images into three states: neutral, happy, and very happy. The smile rating technology detects the face from frames of a video, then predicts the emotion of the face with a trained convolutional neural network (CNN) to compute a smile grade. Previously, the CNN was trained with nearly
Happy
3000 images extracted from videos recorded in previous studies conducted by Lipotec active ingredients. In order to evaluate the grade of the
volunteer’s happiness, they were asked to smile according to the level of satisfaction of their skin after the treatment. Videos about their reaction were taken with a high- resolution camera (Canon Rebel T4i) and analyzed with the smile rating technology. The results showed that by using the new active ingredient, volunteers expressed a 22% bigger smile respect to the placebo group at the end of the study.
Clinical efficacy in preventing the appearance of expression wrinkles in young Asian volunteers A second clinical study was carried out on 60 Asian male and female volunteers, between 25 and 40 years old. These volunteers were divided in two different groups: the active group applied an oil serum containing 0.8% of the new active ingredient, and the placebo group applied an oil serum without any active ingredient. Both groups applied their respective
products twice a day on the face and neck. After 7, 21 and 28 days of treatment, different parameters were evaluated including skin profilometry by PRIMOS on the forehead and nasolabial folds, and neck folds by a dermatologist through visual scoring.
Skin profilometry improvement Very Happy
* B 0 days 28 days
Record video
Extract frames from video
Emotion classification
happiness on video
Figure 4: Frame images of a volunteer analyzed with smile rating technology method at three different states after the treatment. Workflow of the smile rating technology process
PERSONAL CARE April 2023
www.personalcaremagazine.com
Computer
Variation in wrinkle area (%)
Variation in wrinkle cofficient of visibility area (%)
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