Emotion recognition for workforce analytics
Methods and systems for videoconferencing include generating work quality metrics based on emotion recognition of an individual such as a call center agent. The work quality metrics allow for workforce optimization. One example method includes the steps of receiving a video including a sequence of images, detecting an individual in one or more of the images, locating feature reference points of the individual, aligning a virtual face mesh to the individual in one or more of the images based at least in part on the feature reference points, dynamically determining over the sequence of images at least one deformation of the virtual face mesh, determining that the at least one deformation refers to at least one facial emotion selected from a plurality of reference facial emotions, and generating quality metrics including at least one work quality parameter associated with the individual based on the at least one facial emotion.
1. A computer-implemented method for workforce analytics, the method comprising:
receiving a set of videos including a sequence of images, each video of the set of videos including at least one individual;
for each video of the set of videos, detecting a respective individual in one or more of the images;
locating, within each video of the set of videos, feature reference points of the respective individual;
aligning, within each video of the set of videos, a virtual face mesh to the respective individual based at least in part on the feature reference points;
dynamically determining, for each video of the set of videos, over the sequence of images at least one deformation of the virtual face mesh;
determining, for each video of the set of videos, that the at least one deformation refers to at least one facial emotion of the respective individual within the video, the at least one facial emotion identified as matching a facial emotion from a plurality of reference facial emotions;
identifying, for a predetermined period of time commonly represented within the set of videos, a set of facial emotions for the respective individuals within the set of videos;
identifying an emotional status for a target user for the predetermined period of time, the target user interacting with the individuals included in the set of videos, and the emotional status determined based on the set of facial emotions of the individuals within the set of videos; and
generating quality metrics for the target user, the quality metrics including at least one work quality parameter associated with the individual based on the at least one facial emotion.
2. The method of claim 1 , further comprising recording the quality metrics of the individual in an employee record, wherein each of the quality metrics is time-stamped.
3. The method of claim 1 , further comprising aggregating the quality metrics associated with the individual over a predetermined period to produce a work performance characteristic of the individual.
4. The method of claim 1 , wherein the east one work quality parameter includes a tiredness characteristic of the individual.
5. A system, comprising:
a computing device including at least one processor and a memory storing processor-executable codes, which, when implemented by the at least one processor, cause to perform the steps of:
receiving a set of videos including a sequence of images, each video of the set of videos including at least one individual;
for each video of the set of videos, detecting a respective individual in one or more of the images;
locating, within each video of the set of videos, feature reference points of the respective individual;
aligning, within each video of the set of videos, a virtual face mesh to the respective individual based at least in part on the feature reference points;
dynamically determining, for each video of the set of videos, over the sequence of images at least one deformation of the virtual face mesh;
determining, for each video of the set of videos, that the at least one deformation refers to at least one facial emotion of the respective individual within the video, the at least one facial emotion identified as matching a facial emotion from a plurality of reference facial emotions;
identifying, for a predetermined period of time commonly represented within the set of videos, a set of facial emotions for the respective individuals within the set of videos;
identifying an emotional status for a target user for the predetermined period of time, the target user interacting with the individuals included in the set of videos, and the emotional status determined based on the set of facial emotions of the individuals within the set of videos; and
generating quality metrics for the target user, the quality metrics including at least one work quality parameter associated with the individual based on the at least one facial emotion.
6. A non-transitory processor-readable medium having instructions stored thereon, which when executed by one or more processors, cause the one or more processors to implement a method, comprising:
receiving a set of videos including a sequence of images, each video of the set of videos including at least one individual;
for each video of the set of videos, detecting a respective individual one or more of the images;
locating, within each video of the set of videos, feature reference points of the respective individual;
aligning, within each video of the set of videos, a virtual face mesh to the respective individual based at least in part on the feature reference points;
dynamically determining, for each video of the set of videos, over the sequence of images at least one deformation of the virtual face mesh;
determining, for each video of the set of videos, that the at least one deformation refers to at least one facial emotion of the respective individual within the video, the at least one facial emotion identified as matching a facial emotion from a plurality of reference facial emotions;
identifying, for a predetermined period of time commonly represented within the set of videos, a set of facial emotions for the respective individuals within the set of videos;
identifying an emotional status for a target user for the predetermined period of time, the target user interacting with the individuals included in the set of videos, and the emotional status determined based on the set of facial emotions of the individuals within the set of videos; and
generating quality metrics for the target user, the quality metrics including at least one work quality parameter associated with the individual based on the at least one facial emotion.