Digital imaging systems and methods of analyzing pixel data of an image of a skin area of a user for determining dark eye circles
Digital imaging systems and methods are described for analyzing pixel data of an image of a skin area of a user for determining dark eye circles. A plurality of training images of a plurality of individuals are aggregated, each of the training images comprising pixel data of a respective skin area of an individual. A dark eye circles model, trained with the pixel data, is operable to output, across a range of a dark eye circles scale, dark eye circles values associated with a degree of dark eye circles. An image of a user comprising pixel data of at least a portion of a user skin area is received and analyzed, by the dark eye circles model, to determine a user-specific dark eye circles value of the user skin area. A user-specific electronic recommendation addressing at least one feature identifiable within the pixel data is generated and rendered, on a display screen of a user computing device.
1. A digital imaging method of analyzing pixel data of an image of a skin area of a user for determining dark eye circles, the digital imaging method comprising the steps of:
a. aggregating, at one or more processors communicatively coupled to one or more memories, a plurality of training images of a plurality of individuals, each of the training images comprising pixel data of a skin area of a respective individual;
b. training, by the one or more processors with the pixel data of the plurality of training images, a dark eye circles model comprising a dark eye circles scale and operable to output, across a range of the dark eye circles scale, dark eye circles values associated with a degree of dark eye circles ranging from least darkness to most darkness;
c. receiving, at the one or more processors, at least one image of a user, the at least one image captured by a digital camera, and the at least one image comprising pixel data of at least a portion of a user skin area of the user;
d. analyzing, by the dark eye circles model executing on the one or more processors, the at least one image captured by the digital camera to determine a user-specific dark eye circles value of the user skin area;
e. generating, by the one or more processors based on the user-specific dark eye circles value, at least one user-specific electronic recommendation designed to address at least one feature identifiable within the pixel data comprising the at least the portion of the user skin area;
f. rendering, on a display screen of a user computing device, the at least one user-specific recommendation;
g. receiving, at the one or more processors, a new image of the user, the new image captured by the digital camera, and the new image comprising pixel data of at least a portion of a user skin area of the user;
h. analyzing, by the dark eye circles model executing on the one or more processors, the new image captured by the digital camera to determine a new user-specific dark eye circles value of the user skin area;
i. generating, based on the new user-specific dark eye circles value, a new user-specific electronic recommendation or comment regarding at least one feature identifiable within the pixel data of the new image; and
j. rendering, on a display screen of a user computing device of the user, the new user-specific recommendation or comment;
wherein a delta user-specific dark eye circles value is generated based on a comparison between the new user-specific dark eye circles value and the user-specific dark eye circles value, wherein the new user-specific recommendation or comment is further based on the delta user-specific dark eye circles value, and wherein the delta user-specific dark eye circles value, a representation of the delta user-specific dark eye circles value, or a comment based on the delta user-specific dark eye circles value, is rendered on the display screen of the user computing device.
2. The digital imaging method of claim 1 , wherein the at least one user-specific electronic recommendation is displayed on the display screen of the user computing device with a graphical representation of the user's skin as annotated with one or more graphics or textual renderings corresponding to the user-specific dark eye circles value.
3. The digital imaging method of claim 1 , wherein the at least one user-specific electronic recommendation is rendered in real-time or near-real time, during, or after receiving the at least one image having the user skin area.
4. The digital imaging method of claim 1 , wherein the at least one user-specific electronic recommendation comprises a product recommendation for a manufactured product.
5. The digital imaging method of claim 4 , wherein the at least one user-specific electronic recommendation is displayed on the display screen of the user computing device with instructions for treating, with the manufactured product, the at least one feature identifiable in the pixel data comprising the at least the portion of the user skin area.
6. The digital imaging method of claim 4 , further comprising the steps of:
initiating, based on the product recommendation, the manufactured product for shipment to the user.
7. The digital imaging method of claim 4 , further comprising the steps of:
generating, by the one or more processors, a modified image based on the at least one image, the modified image depicting how the user's skin is predicted to appear after treating the at least one feature with the manufactured product; and
rendering, on the display screen of the user computing device, the modified image.
8. The digital imaging method of claim 1 , wherein the at least one user-specific electronic recommendation is displayed on the display screen of the user computing device with instructions for treating the at least one feature identifiable in the pixel data comprising the at least the portion of the user skin area.
9. The digital imaging method of claim 1 , wherein the dark eye circles model is an artificial intelligence (AI) based model trained with at least one AI algorithm.
10. The digital imaging method of claim 1 ,
wherein the dark eye circles model is further trained, by the one or more processors with the pixel data of the plurality of training images, to output one or more location identifiers indicating one or more corresponding body area locations of respective individuals, and
wherein the dark eye circles model, executing on the one or more processors and analyzing the at least one image of the user, determines a location identifier indicating a body area location of the user skin area.
11. The digital method of claim 10 , wherein the body area location comprises the user's cheek, the user's neck, the user's head, and the user's face.
12. The digital method of claim 1 , wherein training, by the one or more processors with the pixel data of the plurality of training images, the dark eye circles model comprises training the dark eye circles model to detect a darkness amount of skin from a body area location of the user to determine the user-specific dark eye circles value of the user skin area.
13. The digital method of claim 1 , wherein training, by the one or more processors with the pixel data of the plurality of training images, the dark eye circles model comprises training the dark eye circles model to detect a folding amount of skin within the skin area to determine the user-specific dark eye circles value of the user skin area.
14. The digital imaging method of claim 1 , wherein a delta user-specific dark eye circles value is generated based on a comparison between the new user-specific dark eye circles value and the user-specific dark eye circles value, wherein the new user-specific recommendation comprises a recommendation of a hair removal product or hair removal technique for the user corresponding to the delta user-specific dark eye circles value.
15. The digital method of claim 1 , wherein the user computing device receives the at least one image the user-specific recommendation on the display screen of the user computing device.