A 5-hour follow-up of the impact on ageing facial signs of some foundations in Japanese women through automatically analysed selfie pictures.

Objective: To explore the possibility of an automatic grading algorithm to detect and quantify, from selfie pictures, the subtle changes of facial signs brought by the application of a foundation.

Material And Methods: A total of 270 Japanese differently aged women (30-54y) living in four different Japanese cities took selfies before, immediately and 5 h after having applied their own foundation, with their own routine.

Results: The analysis of 810 selfie pictures revealed (3 times × 270 women) that, prior to applications, all women presented a low grade of facial ageing.

Developing an Artificial Intelligence (A.I)-based descriptor of facial appearance that fits with the assessments of makeup experts.

Objective: To develop an A.I-based automatic descriptor that detects and grades, from selfie pictures, 23 facial signs, hairs included, as a help to making-up procedures.

Material And Methods: The selfie images taken in very different conditions by 3326 women and men were used to create (90% of dataset) and validate (10% of dataset) a new algorithm architecture to appraise and grade 23 different facial signs such as lips, nose, eye color, eyebrows, eyelashes, and hair color as defined by makeup artists.

Skin moisture assessment using Hydration Sensor Patches coupled with smartphones via Near Field Communication (NFC). A pilot study with the first generation of patches that allow self-recordings of skin hydration.

Objectives: To evaluate the potency of a new skin hydration sensor patch in the fast self-recording of skin hydration.

Material And Methods: The Skin Hydration Sensor Patch (SHSP) turns the user's smartphone into a wireless skin moisture measuring device. The SHSP combines a capacitive measurement unit and Near Field Communication technology (NFC) for transmitting data and energy.