Abstract: Digital imaging systems and methods are described for analyzing pixel data of an image of a shaving stroke for determining pressure being applied to a user's skin. A plurality of training images of a plurality of individuals are aggregated, each of the training images comprising pixel data of a respective individual when shaving with a shaving razor. A pressure model, trained with the pixel data, is operable to determine pressure being applied to the respective individual's skin. An image of a user when shaving with the shaving razor is received and analyzed, by the pressure model, to determine a user-specific pressure being applied to the user's skin by the shaving razor when a user shaving stroke is taken. A user-specific electronic recommendation to address at least one feature identifiable within the pixel data is generated and rendered, on a display screen of a user computing device.

Abstract: Artificial intelligence based systems and methods are described for analyzing pixel data of an image of a user's body for determining a hair density value of the user's hair. An example method includes aggregating a plurality of training images of a plurality of users' bodies, and training a hair density model operable to output a hair density value. The example method may further include receiving an image of a user comprising pixel data of a portion of the user's body or body area, and analyzing, by the hair density model, the image to determine a user-specific hair density value of the user's hair. The example method may further include generating a product recommendation, wherein the product is designed to address a feature identifiable within the pixel data of the user's body or body area.

Abstract: Artificial intelligence based systems and methods are described for analyzing pixel data of an image of a user's body for determining a hair growth direction value of the user's hair. An example method includes aggregating a plurality of training images of a plurality of users' bodies, and training a hair growth direction model operable to output a hair growth direction value. The example method may further include receiving an image of a user comprising pixel data of a portion of the user's body or body area, and analyzing, by the hair growth direction model, the image to determine a user-specific hair growth direction value of the user's hair. The example method may further include generating a product recommendation, wherein the product is designed to address a feature identifiable within the pixel data of the user's body or body area.

Abstract: A digital imaging method of analyzing pixel data of an image of a user's body for determining a user-specific hair removal efficiency value after removing hair is disclosed. The digital imaging method includes: aggregating training images of respective individuals' bodies after removing hair; training, using the pixel data of the training images, a hair removal efficiency model to determine hair removal efficiency values associated with a degree of hair removal efficiencies ranging from a least efficient to a most efficient; receiving an image of a user's body after removing hair; analyzing by the user image using the hair removal efficiency model to determine a user-specific hair removal efficiency value; generating a user-specific electronic recommendation designed to address a feature identifiable within the pixel data of the user's body based on the user-specific hair removal efficiency value; and rendering the recommendation on a display screen of a user computing device.

Abstract: Digital imaging systems and methods are described for determining a user-specific skin irritation value of a user's skin after removing hair. An example method may be performed by one or more processors and may include aggregating training images comprising pixel data of skin of individuals after removing hair. A skin irritation model may be trained using the training images to output skin irritation values associated with a degree of skin irritation from least to most irritation. The method may include receiving an image of a user including pixel data of the user's skin after hair is removed from the skin, analyzing the image using the skin irritation model to determine a user-specific skin irritation value, generating a user-specific recommendation designed to address a feature identifiable within the pixel data of the user's skin, and rendering the recommendation on a display screen of a user computing device.

Abstract: Digital imaging systems and methods are described for analyzing pixel data of an image of a skin area of a user for determining body contour. 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 body contour model, trained with the pixel data, is operable to output, across a range of a body contour scale, body contour values associated with a degree of body contour. An image of a user comprising pixel data of at least a portion of a user skin area is received and analyzed, by the body contour model, to determine a user-specific body contour 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.

Abstract: Digital imaging systems and methods are described for analyzing pixel data of an image of a skin area of a user for determining skin roughness. 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 skin roughness model, trained with the pixel data, is operable to output, across a range of a skin roughness scale, skin roughness values associated with a degree of skin roughness. An image of a user comprising pixel data of at least a portion of a user skin area is received and analyzed, by the skin roughness model, to determine a user-specific skin roughness 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.

Abstract: 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.

Abstract: Digital imaging systems and methods are described for analyzing pixel data of an image of a skin area of a user for determining skin pore size. 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 skin pore size model, trained with the pixel data, is operable to output, across a range of a skin pore size scale, skin pore size values associated with a degree of skin pore size. An image of a user comprising pixel data of at least a portion of a user skin area is received and analyzed, by the skin pore size model, to determine a user-specific skin pore size 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.

Abstract: Digital imaging systems and methods are described for analyzing pixel data of an image of a skin area of a user for determining skin dryness. 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 skin dryness model, trained with the pixel data, is operable to output, across a range of a skin dryness scale, skin dryness values associated with a degree of skin dryness. An image of a user comprising pixel data of at least a portion of a user skin area is received and analyzed, by the skin dryness model, to determine a user-specific skin dryness 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.

Abstract: Digital imaging systems and methods are described for analyzing pixel data of an image of a skin area of a user for determining skin hyperpigmentation. 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 skin hyperpigmentation model, trained with the pixel data, is operable to output, across a range of a skin hyperpigmentation scale, skin hyperpigmentation values associated with a degree of skin hyperpigmentation. An image of a user comprising pixel data of at least a portion of a user skin area is received and analyzed, by the skin hyperpigmentation model, to determine a user-specific skin hyperpigmentation 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.

Abstract: Digital imaging systems and methods are described for analyzing pixel data of an image of a skin area of a user for determining skin puffiness. 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 skin puffiness model, trained with the pixel data, is operable to output, across a range of a skin puffiness scale, skin puffiness values associated with a degree of skin puffiness. An image of a user comprising pixel data of at least a portion of a user skin area is received and analyzed, by the skin puffiness model, to determine a user-specific skin puffiness 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.

Abstract: Digital imaging systems and methods are described for analyzing pixel data of an image of a skin area of a user for determining skin oiliness. 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 skin oiliness model, trained with the pixel data, is operable to output, across a range of a skin oiliness scale, skin oiliness values associated with a degree of skin oiliness. An image of a user comprising pixel data of at least a portion of a user skin area is received and analyzed, by the skin oiliness model, to determine a user-specific skin oiliness 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.

Abstract: A digital imaging method of analyzing pixel data of an image of a user's body for determining a user-specific trapped hair value before removing hair is disclosed. The digital imaging method includes: aggregating training images of respective individuals' bodies before removing hair; training, using the pixel data of the training images, a trapped hair model to determine trapped hair values associated with a trapped hair scale ranging from least trapped hair to most trapped hair; receiving an image of a user's body before removing hair; analyzing the user image using the trapped hair model to determine a user-specific trapped hair value; generating a user-specific electronic recommendation designed to address a feature identifiable within the pixel data of the user's body based on the user-specific trapped hair value; and rendering the recommendation on a display screen of a user computing device.

Abstract: Digital imaging systems and methods are described for determining a user-specific skin redness value of a user's skin after removing hair. An example method may be performed by one or more processors and may include aggregating training images comprising pixel data of skin of individuals after removing hair. A skin redness model may be trained using the training images to output skin redness values associated with a degree of skin redness from least to most red. The method may include receiving an image of a user including pixel data of the user's skin after hair is removed from the skin, analyzing the image using the skin redness model to determine a user-specific skin redness value, generating a user-specific recommendation designed to address a feature identifiable within the pixel data of the user's skin, and rendering the recommendation on a display screen of a user computing device.

Abstract: Digital imaging systems and methods are described for analyzing pixel data of an image of a shaving stroke for determining pressure being applied to a user's skin. A plurality of training images of a plurality of individuals are aggregated, each of the training images comprising pixel data of a respective individual when shaving with a shaving razor. A pressure model, trained with the pixel data, is operable to determine pressure being applied to the respective individual's skin. An image of a user when shaving with the shaving razor is received and analyzed, by the pressure model, to determine a user-specific pressure being applied to the user's skin by the shaving razor when a user shaving stroke is taken. A user-specific electronic recommendation to address at least one feature identifiable within the pixel data is generated and rendered, on a display screen of a user computing device.

Abstract: Digital imaging systems and methods are described for analyzing pixel data of an image of a shaving stroke for determining pressure being applied to a user's skin. A plurality of training images of a plurality of individuals are aggregated, each of the training images comprising pixel data of a respective individual when shaving with a shaving razor. A pressure model, trained with the pixel data, is operable to determine pressure being applied to the respective individual's skin. An image of a user when shaving with the shaving razor is received and analyzed, by the pressure model, to determine a user-specific pressure being applied to the user's skin by the shaving razor when a user shaving stroke is taken. A user-specific electronic recommendation to address at least one feature identifiable within the pixel data is generated and rendered, on a display screen of a user computing device.

Abstract: A digital imaging method of analyzing pixel data of an image of a user's body for determining a user-specific trapped hair value before removing hair is disclosed. The digital imaging method includes: aggregating training images of respective individuals' bodies before removing hair; training, using the pixel data of the training images, a trapped hair model to determine trapped hair values associated with a trapped hair scale ranging from least trapped hair to most trapped hair; receiving an image of a user's body before removing hair; analyzing the user image using the trapped hair model to determine a user-specific trapped hair value; generating a user-specific electronic recommendation designed to address a feature identifiable within the pixel data of the user's body based on the user-specific trapped hair value; and rendering the recommendation on a display screen of a user computing device.

Abstract: Digital imaging systems and methods are described for analyzing pixel data of an image of a skin area of a user for determining skin laxity. 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 skin laxity model, trained with the pixel data, is operable to output, across a range of a skin laxity scale, skin laxity values associated with a degree of skin laxity. An image of a user comprising pixel data of at least a portion of a user skin area is received and analyzed, by the skin laxity model, to determine a user-specific skin laxity 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.

Abstract: Digital imaging systems and methods are described for analyzing pixel data of an image of a shaving stroke for determining pressure being applied to a user's skin. A plurality of training images of a plurality of individuals are aggregated, each of the training images comprising pixel data of a respective individual when shaving with a shaving razor. A pressure model, trained with the pixel data, is operable to determine pressure being applied to the respective individual's skin. An image of a user when shaving with the shaving razor is received and analyzed, by the pressure model, to determine a user-specific pressure being applied to the user's skin by the shaving razor when a user shaving stroke is taken. A user-specific electronic recommendation to address at least one feature identifiable within the pixel data is generated and rendered, on a display screen of a user computing device.