Emotional reaction sharing

A method of emotional reaction sharing, the method comprising:

• receiving, at a user reaction distribution service, a first set of landmark points, a second set of landmark points, and a mood of a first user from a client device, wherein:
• the first set of landmark points represents a set of facial features of the first user at a first point in time and the second set of landmark points represents the set of facial features of the first user at a second point in time while the first user is viewing content through the client device, and
• the mood is identified at the client device from audio of the first user while the first user is viewing the content through the client device;
• evaluating, at the user reaction distribution service, the first set of landmark points and the second set of landmark points, using a facial expression recognition algorithm that maps changes in location of landmark points to facial movements indicative of facial expressions, to identify a facial expression of the first user while the first user is viewing the content;
• verifying the facial expression of the first user based upon the mood;
• identifying, at the user reaction distribution service, a set of facial expressions of other users viewing the content during a time interval between the first point in time and the second point in time based upon landmark points received from client devices of the other users, wherein:
• the client device of the first user and the client devices of the other users define a group of client devices, and
• the facial expression of the first user and the set of facial expressions of other users define a group of facial expressions;
• ranking, at the user reaction distribution service, the group of facial expressions to determine a most frequently occurring facial expression, amongst the group of facial expressions, during the time interval;
• and
• sending, from the user reaction distribution service, the most frequently occurring facial expression to a plurality of client devices amongst the group of client devices in real-time during viewing of the content by the first user and by the other users.

A non-transitory machine readable medium having stored thereon processor-executable instructions that when executed cause performance of operations, the operations comprising:

• receiving, at a user reaction distribution service, a first set of landmark points, a second set of landmark points, and a mood of a first user from a client device, wherein:
• the first set of landmark points represents a set of facial features of the first user at a first point in time and the second set of landmark points represents the set of facial features of the first user at a second point in time while the first user is viewing content through the client device, and
• the mood is identified at the client device from audio of the first user while the first user is viewing the content through the client device;
• evaluating, at the user reaction distribution service, the first set of landmark points and the second set of landmark points, using a facial expression recognition algorithm that maps changes in location of landmark points to facial movements indicative of facial expressions, to identify a facial expression of the first user while the first user is viewing the content;
• verifying the facial expression of the first user based upon the mood;
• identifying, at the user reaction distribution service, a set of facial expressions of other users viewing the content during a time interval between the first point in time and the second point in time based upon landmark points received from client devices of the other users, wherein:
• the client device of the first user and the client devices of the other users define a group of client devices, the first user and the other users define a group of users, and the facial expression of the first user and the set of facial expressions of other users define a group of facial expressions;
• ranking, at the user reaction distribution service, the group of facial expressions to determine a most frequently occurring facial expression, amongst the group of facial expressions, during the time interval;
• and
• sending, from the user reaction distribution service, the most frequently occurring facial expression to a plurality of client devices amongst the group of client devices in real-time during viewing of the content by the first user and by the other users.